The study addresses the challenge of trustworthiness in long-context large language models (LLMs) by enabling them to generate specific citations for their answers, thus improving verification and reducing hallucinations. The authors introduce a novel method called CoF (Coarse to Fine) for automatically generating training data with sentence-level citations, which is used to train two new models, LongCite-8B and LongCite-9B. These models outperform advanced proprietary models in citation accuracy and overall correctness. The research emphasizes the importance of citations for enhancing the reliability of LLM outputs in various applications.
Description: Figure 1 visually compares chunk-level and sentence-level citations, highlighting the user experience benefits of the latter.
Relevance: The figure effectively demonstrates the need for fine-grained citations, setting the stage for the research's focus on improving citation precision and user verification.
Description: Table 2 showcases the performance of various models on citation quality metrics, highlighting the superiority of the LongCite models.
Relevance: This table is crucial for understanding the competitive advantage of LongCite models in generating accurate citations, supporting the study's claims of improved trustworthiness.
The research successfully enhances the trustworthiness of long-context LLMs by developing a novel citation generation approach through the CoF pipeline and LongCite models. These advancements demonstrate significant improvements in citation accuracy and answer correctness, laying a foundation for future research in long-context question answering with citations (LQAC). The study's findings underscore the potential for citation-based training to improve the reliability and verifiability of LLM outputs, with implications for various applications that require accurate information and transparent verification. Future work should explore diverse citation generation techniques and address current limitations to further advance the field.
This abstract introduces a research project focused on improving the trustworthiness of long-context large language models (LLMs) by enabling them to provide specific citations for their answers. The authors highlight the issue of LLMs sometimes generating inaccurate information (hallucinations) and the difficulty in verifying their outputs due to the lack of citations. They propose a new method called CoF (Coarse to Fine) to automatically generate training data with sentence-level citations, which they use to train new models (LongCite-8B and LongCite-9B). These models demonstrate improved citation accuracy and a reduction in hallucinations, surpassing even advanced proprietary models like GPT-4o.
The abstract effectively establishes the problem of trustworthiness in long-context LLMs, highlighting both the issue of hallucinations and the lack of citations for verification.
The abstract clearly presents the CoF pipeline as a novel solution for generating citation-rich training data and highlights the impressive performance of the LongCite models, surpassing even proprietary models.
The abstract effectively summarizes the key contributions of the research within a reasonable length, providing sufficient detail to understand the problem, approach, and results.
While the abstract mentions that LongCite models surpass GPT-4o, providing specific numbers (e.g., percentage improvement in citation F1 score) would strengthen the claims.
Rationale: Quantifying the improvement would make the results more impactful and convincing for readers.
Implementation: Include a specific metric and the percentage improvement achieved by LongCite models compared to GPT-4o.
The abstract could briefly mention the broader implications of this research, such as its potential to enhance the reliability of LLMs in various applications.
Rationale: Highlighting the potential impact would make the research more appealing to a wider audience.
Implementation: Add a sentence or two about the potential applications or benefits of this research in fields where LLM reliability is crucial.
While the abstract focuses on the positive results, briefly acknowledging any limitations of the approach or the models would enhance the transparency of the research.
Rationale: Acknowledging limitations demonstrates scientific rigor and provides a more balanced perspective.
Implementation: Add a sentence mentioning any limitations, such as the computational cost of the CoF pipeline or the dependence on existing LLMs for data generation.
This introduction sets the stage for a research paper focused on enabling long-context large language models (LLMs) to generate citations, thereby enhancing their trustworthiness and verifiability. It begins by acknowledging the impressive capabilities of these LLMs in handling vast amounts of text but points out a critical limitation: the absence of citations in their responses. This lack of traceability makes it difficult for users to verify the information provided by LLMs, especially considering their susceptibility to generating inaccurate or fabricated content (hallucinations). The authors then discuss existing methods for generating citations in other domains, such as web browsing and open-domain question answering, but highlight their shortcomings in the context of long-context scenarios. These limitations include compromised answer quality due to incomplete context information in retrieval-based methods and increased user waiting time in post-hoc approaches. Moreover, the citations generated by these methods often lack granularity, referring to entire web pages or large text chunks, making it challenging for users to pinpoint the specific supporting evidence. The introduction concludes by emphasizing the need for a more effective approach that allows long-context LLMs to directly generate accurate responses with fine-grained, sentence-level citations, setting the stage for the research presented in the paper.
The introduction effectively establishes the problem of missing citations in long-context LLMs and clearly articulates its impact on user trust and the difficulty of verifying LLM-generated information.
The introduction provides a concise but thorough overview of existing methods for citation generation, acknowledging their strengths while clearly outlining their limitations in the context of long-context scenarios.
The introduction clearly defines the scope of the research, focusing specifically on long-context question answering with citations (LQAC) and emphasizing the goal of generating fine-grained, sentence-level citations.
While the introduction effectively describes the problem and limitations of existing methods, including specific examples of how these limitations manifest in real-world scenarios would enhance clarity and reader engagement.
Rationale: Concrete examples would make the challenges more tangible and relatable for readers, strengthening the motivation for the proposed research.
Implementation: Include a brief example illustrating how a coarse citation (e.g., referring to a large chunk of text) makes it difficult for a user to verify the LLM's answer, contrasting it with a fine-grained citation that directly points to the specific supporting sentence.
The introduction could more explicitly emphasize the unique aspects of the proposed research compared to existing methods, showcasing its potential to overcome the identified limitations.
Rationale: Highlighting the novelty would make the research more compelling and attract readers' attention to its potential contributions.
Implementation: Add a sentence or two explicitly stating how the proposed approach differs from existing methods and why it is expected to be more effective in addressing the challenges of LQAC, such as by leveraging the inherent capabilities of long-context LLMs or by introducing a novel training methodology.
The introduction briefly mentions the importance of trustworthy LLMs but could expand on the potential impact of this research in specific domains or applications where accurate information and verification are crucial.
Rationale: Elaborating on the potential impact would broaden the appeal of the research and highlight its relevance to a wider audience.
Implementation: Include a brief discussion of how improved citation generation in long-context LLMs could benefit specific fields, such as legal research, scientific writing, or fact-checking, where the ability to trace information back to its source is essential.
This section describes the creation and evaluation of LongBench-Cite, a benchmark designed to test how well long-context language models (LLMs) can answer questions and provide specific citations from a large body of text. It explains the challenge of making sure LLMs don't just give accurate answers but also back them up with precise references to the source material. The section details how the benchmark was built using existing datasets, covering various tasks like question answering and summarization. It also outlines the metrics used to assess both the accuracy of the answers and the quality of the citations, including how well they support the answer and how specific they are.
The authors have carefully designed LongBench-Cite to cover a wide range of tasks and languages, using existing datasets to ensure a robust and representative evaluation of LQAC capabilities.
The emphasis on sentence-level citations is a significant strength, as it promotes fine-grained traceability and enhances the user's ability to verify the LLM's answers.
The use of GPT-4o for evaluating both correctness and citation quality is a strength, as it leverages the capabilities of a powerful language model to provide a more nuanced and reliable assessment.
The section mentions using 128-token chunks for initial citation generation but doesn't explain why this specific size was chosen.
Rationale: Providing a rationale for the chunk size would enhance the transparency of the methodology and allow readers to better understand the design choices.
Implementation: Add a sentence or two explaining the reasoning behind the 128-token chunk size, considering factors such as computational efficiency, the typical length of sentences, or the desired level of granularity for initial citation generation.
While using GPT-4o for evaluation is a strength, the section could acknowledge the inherent limitations of relying on an automated system for assessing complex aspects like correctness and citation quality.
Rationale: Acknowledging limitations demonstrates scientific rigor and provides a more balanced perspective on the evaluation methodology.
Implementation: Add a brief discussion of the potential limitations of using GPT-4o for evaluation, such as its susceptibility to biases or its inability to fully capture the nuances of human judgment. Consider mentioning the need for human evaluation to complement the automated assessment, especially for tasks involving subjective interpretation or complex reasoning.
The section mentions using prompts for GPT-4o evaluation but doesn't provide specific examples or details about the prompting strategy.
Rationale: Providing more details on the prompting strategy would enhance the reproducibility of the evaluation methodology and allow readers to better understand how GPT-4o was used for assessment.
Implementation: Include examples of the prompts used for evaluating correctness and citation quality, highlighting the specific instructions and the expected format of GPT-4o's responses. Discuss any challenges or considerations in designing effective prompts for these tasks.
Figure 1 visually compares two methods of citing sources in long-context question answering: chunk-level citations and sentence-level citations. It uses two panels, each showing a question, a context, and an answer with citations. Panel (a) illustrates chunk-level citations, where the context is divided into fixed-size chunks, and citations refer to these chunks. However, this can lead to incomplete sentences in the answer, as the chunk boundaries might cut off sentences. Panel (b) shows sentence-level citations, where citations refer to specific sentences in the context, ensuring that the cited text is complete and grammatically correct. The figure uses emoticons to emphasize the user experience: a sad face for the less user-friendly chunk-level citations and a happy face for the more user-friendly sentence-level citations.
Text: "As illustrated in Figure 1, we consider two types of citations:"
Context: The authors are introducing the concept of chunk-level and sentence-level citations and using Figure 1 to visually represent these concepts.
Relevance: This figure is crucial for understanding the motivation behind the research. It clearly demonstrates the problem with existing chunk-level citations and highlights the advantage of using sentence-level citations for a better user experience. It sets the stage for the paper's focus on developing methods to generate fine-grained, sentence-level citations.
Table 1 provides statistics about the datasets used in LongBench-Cite, a benchmark for evaluating long-context question answering with citations. It lists six datasets, each with its corresponding task (e.g., single-document question answering, multi-document question answering, summarization), the source of the context (e.g., Wikipedia, government reports), the average length of the contexts in words or characters, the language of the dataset (English or Chinese), and the number of data points in each dataset.
Text: "The detailed data statistics are listed in Table 1."
Context: The authors are describing the datasets used in their benchmark and referring to Table 1 for detailed information about these datasets.
Relevance: This table is essential for understanding the scope and diversity of the benchmark used to evaluate the models. It provides a clear overview of the datasets, their characteristics, and the tasks they cover, allowing readers to assess the generalizability of the results.
Table 2 presents the performance of different language models on the LongBench-Cite benchmark, focusing on their ability to generate accurate and relevant citations. It compares various models, including both proprietary models (like GPT-4o) and open-source models, using metrics such as citation recall, precision, F1 score, and citation length. The table highlights the best and second-best performing models for each metric across different datasets.
Text: "The evaluation results of citation quality and correctness are presented in Table 2 and Table 3, respectively."
Context: We select LAC-S strategy as the default setting due to its efficiency, losslessness of context information, and no reliance on additional retrieval systems. A further discussion about the pros and cons of different LQAC strategies can be found in Sec. 3.2. As illustrated in Figure 10, we number each sentence senti in the context by adding a prefix '<Ci>' and prompt the LLM with one demonstration. The evaluation results of citation quality and correctness are presented in Table 2 and Table 3, respectively. Our findings are as follows:
Relevance: This table is crucial for understanding the current state of long-context question answering with citations (LQAC). It provides a direct comparison of different language models' abilities to generate accurate and relevant citations, highlighting the strengths and weaknesses of existing models and setting the stage for the authors' proposed approach.
Table 3 compares the correctness of different language models in answering questions based on long contexts, both with and without the requirement to generate citations. It presents the correctness scores (C) for models in the LQAC setting, the correctness scores (CLQA) in the vanilla long-context QA setting (without citations), and the correctness ratio (CR), which indicates whether adding citations improves or hurts the model's ability to answer questions correctly. The table highlights cases where adding citations improves correctness in green and cases where it hurts correctness in red.
Text: "The evaluation results of citation quality and correctness are presented in Table 2 and Table 3, respectively."
Context: We select LAC-S strategy as the default setting due to its efficiency, losslessness of context information, and no reliance on additional retrieval systems. A further discussion about the pros and cons of different LQAC strategies can be found in Sec. 3.2. As illustrated in Figure 10, we number each sentence senti in the context by adding a prefix '<Ci>' and prompt the LLM with one demonstration. The evaluation results of citation quality and correctness are presented in Table 2 and Table 3, respectively. Our findings are as follows:
Relevance: This table is essential for understanding the impact of citation generation on the overall correctness of long-context question answering. It directly addresses the concern that requiring models to generate citations might compromise their ability to answer questions accurately. The table helps to assess whether adding citations is beneficial or detrimental to the model's performance.
This section introduces CoF, a method for automatically creating training data to teach long-context language models (LLMs) how to provide citations for their answers. It's like giving the LLM a set of practice questions with answers and showing it exactly where in a long text each part of the answer comes from. The method works in stages: first, it generates a question and answer from a long text. Then, it finds relevant sections of the text (chunks) related to the answer. Next, it pinpoints the specific sentences within those chunks that directly support the answer. Finally, it filters out any examples where the answer doesn't have enough supporting citations. The authors test this method and show that it helps LLMs generate more accurate citations without sacrificing the quality of the answers.
The section provides a step-by-step explanation of the CoF pipeline, making it easy for readers to understand the process of generating citation-rich training data.
The authors provide clear justifications for the design choices in CoF, such as the use of a post-hoc approach, the coarse-to-fine strategy, and the data filtering step.
The authors thoroughly validate CoF on LongBench-Cite, comparing it with various other LQAC strategies and demonstrating its superiority in terms of citation quality and answer correctness.
The section mentions using sentences in the answer to retrieve chunks but doesn't provide details about the specific retrieval method or the criteria used for selecting relevant chunks.
Rationale: Providing more details about the retrieval process would enhance the transparency of the methodology and allow readers to better understand how relevant chunks are identified.
Implementation: Include a brief description of the retrieval method used (e.g., keyword-based search, semantic similarity), the specific criteria for selecting relevant chunks (e.g., overlap with answer sentences, relevance score), and any thresholds or parameters involved in the retrieval process.
The section doesn't explicitly discuss how the quality of the retrieved chunks might affect the performance of CoF, particularly the accuracy of the generated citations.
Rationale: Acknowledging the potential impact of retrieval quality would provide a more comprehensive understanding of the limitations and potential challenges of the CoF pipeline.
Implementation: Add a brief discussion of how the accuracy and relevance of the retrieved chunks might influence the citation generation process. Consider mentioning potential issues such as retrieving irrelevant chunks or missing relevant chunks, and how these issues might affect the citation recall and precision. Discuss any strategies used to mitigate these risks, such as using multiple retrieval methods or adjusting retrieval parameters.
While the section describes the CoF pipeline and its validation, it would be helpful to include examples of the actual training data generated by CoF, showcasing the format and quality of the question-answer pairs with sentence-level citations.
Rationale: Providing examples of generated data would give readers a more concrete understanding of the output of CoF and allow them to assess the quality and usefulness of the training data.
Implementation: Include a few examples of question-answer pairs with sentence-level citations generated by CoF. Choose examples that illustrate different aspects of the data, such as single-sentence answers, multi-sentence answers, and answers with varying levels of citation complexity. Briefly explain the rationale for choosing these examples and highlight any interesting or challenging aspects of the citation generation process.
Figure 2 provides a visual overview of the CoF (Coarse to Fine) pipeline, a method for automatically creating training data for long-context question answering with citations (LQAC). The pipeline has four main steps, each represented by a panel in the diagram: **(a) QA Instance Generation:** This step starts with a long text document. The CoF pipeline uses an existing large language model (LLM) to generate a question and its corresponding answer from the document. Think of it like asking the LLM to come up with a quiz question and its answer based on a textbook chapter. **(b) Chunk-Level Citation Generation:** The document is divided into chunks of a fixed size (128 tokens). The answer generated in the previous step is used to retrieve relevant chunks from the document. The LLM then adds citations to the answer, referring to these chunks. It's like highlighting the sections in the textbook chapter that support the answer to the quiz question. **(c) Sentence-Level Citation Extraction:** This step refines the citations to be more precise. For each chunk cited in the previous step, the LLM identifies the specific sentences that support the answer. This ensures that the citations are accurate and point to the exact source of information. It's like narrowing down the highlighted sections in the textbook to the specific sentences that provide the answer. **(d) Data Filtering:** Finally, the pipeline filters out any instances where the answer has too few citations. This ensures that the training data only includes examples where the LLM has found sufficient evidence in the document to support its answer. It's like removing any quiz questions that don't have enough supporting information in the textbook.
Text: "As illustrated in Figure 2, CoF consists of four steps:"
Context: The authors are introducing the CoF pipeline and using Figure 2 to visually explain its four-step process.
Relevance: Figure 2 is essential for understanding how the CoF pipeline works. It provides a clear visual representation of the process, making it easier to grasp the complex steps involved in generating training data for LQAC. The figure helps readers understand how the pipeline leverages existing LLMs to automatically create high-quality training data with precise citations.
Table 4 compares different strategies for generating answers and citations in long-context question answering, using the GLM-4 language model. It shows how well each strategy performs on various datasets, measuring both the quality of the citations and the correctness of the answers. The table is divided into two main categories: **One-Pass Methods:** These methods try to generate the answer and citations simultaneously in a single step. They include LAC-C/LAC-S (generating chunk-level/sentence-level citations while reading the entire context) and RAC-C/RAC-S (generating citations while reading only a few retrieved chunks/sentences). **Post-Hoc Methods:** These methods first generate the answer and then add citations in a separate step. They include post-LC-C/post-LC-S (adding citations after generating the answer by searching the entire context) and post-RC-C/post-RC-S (adding citations after generating the answer by searching only a few retrieved chunks/sentences). The table also includes CoF, the authors' proposed pipeline. The table uses several metrics to evaluate the strategies: * **Citation F1:** This measures the overall quality of the citations, considering both recall (whether all necessary citations are provided) and precision (whether all citations are relevant). * **Correctness (C):** This measures how accurate and comprehensive the answer is. * **Correctness Ratio (CR):** This compares the correctness of the answer in the LQAC setting (with citations) to the correctness in the vanilla long-context QA setting (without citations). A CR greater than 100% means that adding citations improved the answer's correctness. * **Citation Length (CL):** This measures the average number of tokens in the cited snippets, indicating the granularity of the citations. Shorter citation lengths generally mean more precise citations. The table highlights the best performing method for each metric on each dataset.
Text: "The results in Table 4 show that:"
Context: The authors are discussing the results of their experiments comparing different LQAC strategies and referring to Table 4 for the specific data.
Relevance: Table 4 is crucial for understanding the strengths and weaknesses of different approaches to LQAC. It provides a direct comparison of various strategies, highlighting the trade-offs between citation quality, answer correctness, and efficiency. The table supports the authors' claim that their proposed CoF pipeline achieves a good balance between these factors.
This section details the experiments conducted to train long-context LLMs to generate citations alongside their answers, aiming to improve their trustworthiness and verifiability. The authors fine-tuned two open-source long-context models, GLM-4-9B and Llama-3.1-8B, using a combination of their newly created LongCite-45k dataset (specifically designed for long-context question answering with citations) and general SFT instances from ShareGPT. They trained separate models, LongSFT-9B and LongSFT-8B, using only the long-context question-answer pairs from LongCite-45k to compare the impact of citation-based training on answer correctness. The section presents the results of these experiments, highlighting the improved citation quality and answer correctness achieved by the LongCite models compared to various proprietary and open-source models.
The authors conducted thorough experiments, training multiple models with different data configurations and evaluating them on a comprehensive benchmark, providing a robust assessment of their approach.
The LongCite models demonstrated significant improvements in both citation quality and answer correctness, surpassing even advanced proprietary models, providing strong evidence for the effectiveness of their approach.
The authors went beyond simply reporting the improved correctness and provided a manual analysis of the responses, identifying specific factors contributing to the improvement, such as reduced hallucination and better context utilization.
The section provides details about the training setup but doesn't mention the computational cost involved in training these long-context models.
Rationale: Understanding the computational cost is important for assessing the practicality and scalability of the approach, especially for researchers with limited resources.
Implementation: Include a brief discussion of the computational resources used for training, such as the number of GPUs, training time, and estimated cost. Consider comparing the cost of training LongCite models with that of training vanilla long-context models or using other citation generation methods.
The section mentions using Zhipu Embedding-v2 for retrieval but doesn't explore the impact of using different retrieval methods on the performance of LongCite models.
Rationale: The choice of retrieval method can significantly influence the quality of the retrieved chunks and, consequently, the accuracy of the generated citations. Exploring different retrieval methods would provide a more comprehensive understanding of the factors affecting LongCite performance.
Implementation: Conduct experiments using different retrieval methods, such as BM25, TF-IDF, or other embedding-based methods. Compare the performance of LongCite models trained with different retrieval methods, analyzing the impact on citation quality, answer correctness, and computational cost.
The section focuses on evaluating LongCite models on LongBench-Cite, but it would be beneficial to assess their generalizability to other datasets or tasks.
Rationale: Assessing generalizability is crucial for understanding the robustness and applicability of the approach beyond the specific benchmark used for evaluation.
Implementation: Evaluate LongCite models on other long-context question answering datasets or tasks, such as document summarization with citations or evidence-based reasoning. Analyze the performance of the models on these new tasks, comparing them with other relevant baselines and discussing any limitations or challenges in generalizing the approach.
Table 5 shows the results of an experiment where the authors tested different ways of training a language model called LongCite-9B to generate citations for its answers. They wanted to see if training the model with data that includes citations would make it better at providing those citations. They compared the model's performance when trained with different types of data: standard training data, data without filtering out examples with few citations, and data created using a different method (post-RAC-S). The table shows how well the model performed in each case, using metrics like recall (did it find all the relevant citations?), precision (were the citations it found actually relevant?), F1 score (a combined measure of recall and precision), citation length (how many words were in the citations on average), and correctness (how accurate was the answer itself?).
Text: "The results in Table 5 indicate that LongSFT-9B performs poorly on LQAC task."
Context: The authors are discussing the results of their experiments on training LongSFT-9B with different data and referring to Table 5 for the specific performance metrics.
Relevance: This table is important because it shows that training with citation information is crucial for improving the model's ability to generate citations. It demonstrates that simply training the model on standard data or data without proper filtering doesn't lead to good citation generation. The table also highlights the challenges of using a different data creation method (post-RAC-S), suggesting that the authors' proposed CoF method is more effective for generating suitable training data.
Figure 3 is a bar graph that shows the relationship between the accuracy of a language model's answers and the quality of the citations it provides. The model being analyzed is LongCite-9B. The graph divides the model's answers into three groups based on how correct they are: not very correct (0-0.33), somewhat correct (0.33-0.67), and very correct (0.67-1). For each group, the graph shows the average citation F1 score, which is a measure of how good the citations are. The higher the F1 score, the better the citations. The graph also shows error bars, which represent the variation in citation F1 scores within each group.
Text: "As illustrated in Figure 3, responses with higher correctness typically have higher citation qualities, demonstrating a mutually promoting relationship between these two attributes."
Context: The authors are discussing the correlation between the correctness of the model's answers and the quality of its citations, using Figure 3 to visually represent this relationship.
Relevance: This figure is important because it suggests that training a language model to generate accurate citations also helps it generate more accurate answers. This finding supports the authors' argument that teaching LLMs to cite their sources not only improves the verifiability of their answers but also enhances their overall performance.
This section reviews previous research relevant to the paper's focus on enabling long-context LLMs to generate citations. It covers two main areas: advancements in long-context LLMs and research on question answering with citations. The authors first discuss the progress in developing LLMs capable of handling extensive text, but point out that these models often lack citations, making it difficult to verify their outputs and address potential inaccuracies (hallucinations). They then review existing methods for generating citations in other domains, like open-domain question answering, but highlight their limitations in long-context scenarios. These limitations include compromised answer quality due to incomplete context in retrieval-based methods and increased processing time in post-hoc approaches. The authors also note that existing citation evaluation methods often rely on limited NLI models, while their work uses GPT-4o for more nuanced assessment.
The section effectively connects the reviewed research to the paper's core focus on long-context question answering with citations, highlighting the gaps that the current work aims to address.
The section provides a concise yet informative overview of relevant research areas, covering both the advancements in long-context LLMs and the challenges in citation generation.
The section doesn't just summarize previous research but also critically analyzes existing methods, highlighting their limitations and motivating the need for the proposed approach.
While the section mentions hallucinations as a concern, it could provide a more detailed explanation of this phenomenon and its implications for LLM trustworthiness.
Rationale: A deeper discussion of hallucinations would highlight the importance of citation generation for addressing this issue and make the research more relevant to readers concerned about LLM reliability.
Implementation: Include a brief explanation of what hallucinations are, how they arise in LLMs, and why they are a significant concern, especially in sensitive domains. Provide examples of hallucinations in long-context scenarios and discuss how citations can help to mitigate this problem by allowing users to verify the information provided by LLMs.
The section focuses on the technical aspects of citation generation but could also address the ethical implications of this technology.
Rationale: Considering the ethical implications is crucial for responsible development and deployment of LLMs, especially as they become more integrated into various aspects of society.
Implementation: Include a brief discussion of the potential ethical implications of citation generation in LLMs, such as the risk of bias in citation selection, the potential for misuse of citations to spread misinformation, and the need for transparency and accountability in LLM-generated citations. Consider mentioning guidelines or best practices for ethical citation generation in LLMs.
The section could benefit from connecting the research on citation generation to broader trends in LLM research, such as explainability, transparency, and trustworthiness.
Rationale: Connecting citation generation to these broader trends would highlight its significance beyond the specific task of LQAC and position the research within the larger context of LLM development.
Implementation: Add a paragraph discussing how citation generation contributes to the broader goals of making LLMs more explainable, transparent, and trustworthy. Explain how citations can help users understand the reasoning behind LLM-generated answers, trace information back to its source, and assess the reliability of LLM outputs. Connect these points to the growing emphasis on responsible AI and the need for LLMs that are not only powerful but also accountable and trustworthy.
This conclusion summarizes the paper's key contributions in enhancing the trustworthiness and verifiability of long-context large language models (LLMs) by enabling them to generate citations. It reiterates the problem of LLMs lacking citations, making it difficult to verify their outputs, and highlights the limitations of existing citation generation methods. The authors emphasize the success of their proposed CoF pipeline in automatically constructing a large-scale dataset with fine-grained sentence-level citations (LongCite-45k) and the effectiveness of their trained models, LongCite-8B and LongCite-9B, in generating accurate responses with precise citations in a single output. The conclusion underscores the significance of this work in laying a foundation for future research on long-context question answering with citations (LQAC) and contributing to the development of more reliable and trustworthy LLMs.
The conclusion effectively summarizes the key contributions of the paper within a short paragraph, covering the problem, the proposed approach, the results, and the broader impact.
The conclusion effectively highlights the most important findings of the research, such as the success of the CoF pipeline, the performance of the LongCite models, and the contribution to LQAC research.
The conclusion clearly articulates the broader impact of the research, emphasizing its contribution to the development of more trustworthy and reliable LLMs, a crucial aspect for various applications.
While the conclusion mentions laying a foundation for future research, it could provide more specific directions for future work in LQAC.
Rationale: Providing specific research directions would guide future work in the field and stimulate further exploration of LQAC.
Implementation: Include a few sentences outlining potential areas for future research, such as exploring different citation generation techniques, developing more robust evaluation metrics for LQAC, or investigating the application of LQAC in specific domains like legal research or scientific writing.
While the conclusion focuses on the positive contributions, briefly acknowledging any limitations of the proposed approach or the trained models would enhance the transparency and completeness of the research.
Rationale: Acknowledging limitations demonstrates scientific rigor and provides a more balanced perspective on the research findings.
Implementation: Add a sentence or two mentioning any limitations, such as the computational cost of the CoF pipeline, the dependence on existing LLMs for data generation, or the potential for bias in citation selection. Briefly discuss how these limitations could be addressed in future work.
The conclusion could strengthen the connection between the research and its potential impact on real-world applications by providing specific examples.
Rationale: Connecting the research to real-world applications would make it more impactful and relevant to a wider audience.
Implementation: Include a sentence or two illustrating how the ability of LLMs to generate citations could benefit specific applications, such as fact-checking, academic research, or legal analysis. Provide concrete examples of how citations could enhance the trustworthiness and verifiability of LLM-generated information in these contexts.
This section, presented as Appendix A, provides a concise overview of the large language models (LLMs) evaluated in the research paper. It presents a table listing the model name, specific version used, and the context window size (the amount of text the model can consider at once) for each LLM. This information allows readers to understand the capabilities and limitations of the models being compared in the study.
The section effectively presents the model information in a clear and concise table, making it easy for readers to quickly grasp the key details about the evaluated LLMs.
The table includes the most relevant information for comparing the LLMs in the context of the research, namely the model name, version, and context window size, allowing readers to understand the key differences between the models.
By providing specific model versions, the authors enhance the transparency and reproducibility of their research, allowing others to use the same models or compare their results with different versions.
While the context window size is important, including the model size (e.g., number of parameters) would provide a more complete picture of the models' capabilities and computational requirements.
Rationale: Model size is a key factor influencing both the performance and the computational cost of LLMs. Including this information would allow readers to better understand the trade-offs between model size, performance, and resource requirements.
Implementation: Add a column to the table indicating the model size for each LLM, either in terms of the number of parameters or a descriptive label (e.g., small, medium, large).
Including links to official documentation or model repositories would allow readers to easily access more detailed information about each LLM.
Rationale: Providing links would enhance the usefulness of the section by allowing readers to delve deeper into the specifics of each model, such as its architecture, training data, or available functionalities.
Implementation: Add a column to the table with links to official documentation, model repositories, or other relevant resources for each LLM. Ensure that the links are active and point to the correct resources.
While the table lists the evaluated models, it would be helpful to include a brief explanation of the rationale behind selecting these specific models.
Rationale: Understanding the model selection rationale would provide context for the comparison and allow readers to assess the representativeness of the chosen models.
Implementation: Add a paragraph or a footnote briefly explaining the criteria used for selecting the LLMs included in the table. Consider factors such as model availability, context window size, performance on relevant benchmarks, or the balance between proprietary and open-source models.
Table 8 provides a concise overview of the different large language models (LLMs) evaluated in the research paper. It lists the model name, the specific version used in the experiments, and the context window size for each model. The context window refers to the amount of text the model can consider at once when processing information or answering questions. Imagine it like the model's short-term memory: a larger context window means the model can 'remember' more information from the text it's reading.
Text: "We list the details of our evaluated models in Table 8."
Context: The authors are referring to Table 8 to provide detailed information about the LLMs used in their experiments.
Relevance: This table is essential for understanding the capabilities and limitations of the different LLMs being compared in the research. It provides a quick reference for readers to see which models were used, their specific versions, and how much text they can handle at once. This information is crucial for interpreting the results and understanding the relative performance of different models on the task of long-context question answering with citations.
This section, presented as Appendix B, provides three specific examples to illustrate how training with citation information improves the performance of long-context language models (LLMs) in question answering. Each case study presents a user query and compares the responses generated by two models: one trained with citations (LongCite-9B) and one trained without citations (LongSFT-9B). The examples highlight how LongCite-9B, by learning to locate and cite supporting evidence, generates more accurate, detailed, and comprehensive answers compared to LongSFT-9B, which often hallucinates information or fails to utilize the full context effectively.
The chosen case studies effectively illustrate the benefits of training LLMs with citations, showcasing how LongCite-9B outperforms LongSFT-9B in terms of accuracy, detail, and context utilization.
The section provides a clear side-by-side comparison of the responses generated by the two models, making it easy to see the differences in their performance and the impact of citation-based training.
The section provides a concise analysis of each case study, explaining how the presence of citations contributes to the improved performance of LongCite-9B.
While the case studies qualitatively demonstrate the benefits of LongCite-9B, quantifying the improvements with metrics like ROUGE scores for summarization or accuracy scores for factual correctness would strengthen the analysis.
Rationale: Quantitative measures would provide a more objective and compelling assessment of the performance differences between the two models.
Implementation: Calculate relevant metrics (e.g., ROUGE scores for summarization, accuracy scores for factual correctness) for the responses generated by both models in each case study. Include these metrics in the analysis to provide a quantitative comparison of their performance.
The section could acknowledge that case studies, while illustrative, are limited in their generalizability. A brief discussion of the limitations would enhance the scientific rigor of the analysis.
Rationale: Acknowledging limitations provides a more balanced perspective and encourages readers to consider the broader context of the findings.
Implementation: Add a sentence or two acknowledging that case studies are based on specific examples and might not fully represent the models' performance on a wider range of tasks or datasets. Emphasize the need for further evaluation on larger benchmarks to confirm the generalizability of the observed improvements.
The section presents the queries without much context about the source documents or the specific challenges they pose. Providing more context would help readers better understand the tasks and appreciate the differences in the models' responses.
Rationale: Understanding the context of the queries would allow readers to better assess the complexity of the tasks and the significance of the observed performance differences.
Implementation: Include a brief description of the source documents used in each case study, highlighting their length, complexity, or any specific challenges they pose for question answering. Explain why these particular queries were chosen and how they represent different aspects of the LQAC task.
Table 9 presents a case study comparing the responses of two language models, LongSFT-9B and LongCite-9B, to a question about the locations of Duke Energy and Affiliated Managers Group. It highlights how LongSFT-9B, trained without citation information, hallucinates by incorrectly stating that Duke Energy has an office in Massachusetts, mirroring the location of Affiliated Managers Group. In contrast, LongCite-9B, trained with citations, provides the correct answer, demonstrating the benefit of citation-based training in reducing hallucinations.
Text: "We present three cases in Table 9, 10, and 11 to help interpret the improvement of correctness (the detail interpretation is in Sec. 4.2.1)."
Context: This quote is from Appendix B, where the authors are introducing three case studies to illustrate how training with citations improves the correctness of language models.
Relevance: This case study demonstrates the practical impact of training LLMs with citations. It shows how LongCite-9B, trained with citations, avoids making a factual error that LongSFT-9B, trained without citations, makes. This highlights the importance of citations in grounding the LLM's responses in factual information and reducing the likelihood of generating incorrect or misleading content.
Table 10 presents another case study comparing the responses of LongSFT-9B and LongCite-9B, this time focusing on a summarization task. It shows how LongCite-9B, trained with citations, generates a more detailed and comprehensive summary of a government report compared to LongSFT-9B, which produces a more superficial summary. The table highlights specific parts of the responses in red (coarse summary) and green (detailed summary) to illustrate the differences.
Text: "We present three cases in Table 9, 10, and 11 to help interpret the improvement of correctness (the detail interpretation is in Sec. 4.2.1)."
Context: This quote is from Appendix B, where the authors are introducing three case studies to illustrate how training with citations improves the correctness of language models.
Relevance: This case study demonstrates how training with citations can lead to more comprehensive and informative responses from LLMs. It suggests that LongCite-9B, by learning to identify and cite specific evidence from the text, develops a better understanding of the content and can therefore generate more detailed and insightful summaries.
Table 11 presents a case study comparing the responses of two language models, LongSFT-9B and LongCite-9B, to a request for a one-page summary of a government report. The table highlights how LongCite-9B, trained with citation information, produces a more comprehensive and detailed summary by utilizing information from various parts of the document, while LongSFT-9B, trained without citations, focuses primarily on the beginning and misses key points from other sections.
Text: "We present three cases in Table 9, 10, and 11 to help interpret the improvement of correctness (the detail interpretation is in Sec. 4.2.1)."
Context: The authors are referring to Table 11 as part of a set of case studies to illustrate how training with citation information improves the correctness of LLM responses.
Relevance: This case study demonstrates the practical benefits of training LLMs with citation information. It shows how LongCite-9B, by learning to locate and cite relevant evidence from different parts of a long document, can generate a more comprehensive and informative summary compared to a model trained without citations. This highlights the potential of citation-based training to improve the quality and usefulness of LLM outputs in real-world tasks like summarization.
This section, presented as Appendix C, showcases the specific prompts used throughout the research paper for various purposes, including evaluating the correctness of model-generated answers, assessing the quality of citations, and guiding the models in generating citations. It includes textual prompts designed to elicit responses from both humans and language models, providing instructions, examples, and the expected format for the output. These prompts are crucial for understanding how the authors evaluated their models and how they trained the models to generate citations.
The prompts are well-written and provide clear instructions, making it easy for both human evaluators and language models to understand the task and the expected output.
The section includes prompts for evaluating both answer correctness and citation quality, as well as prompts for guiding the models in generating citations, covering all key aspects of the research.
By presenting the specific prompts used, the authors enhance the transparency and reproducibility of their work, allowing others to understand and potentially replicate the experiments.
While the section presents the final prompts, it would be helpful to include a brief discussion of the challenges encountered in designing effective prompts for these tasks.
Rationale: Prompt engineering is a crucial aspect of working with language models, and discussing the challenges would provide valuable insights for other researchers working on similar tasks.
Implementation: Add a paragraph or a footnote discussing the challenges faced in designing the prompts, such as finding the right level of detail, avoiding bias, or ensuring that the prompts elicit the desired responses. Briefly mention any iterations or refinements made to the prompts during the research process.
The section includes examples of prompts but would benefit from showing more examples of actual model responses to these prompts, illustrating how the models interpret and respond to the instructions.
Rationale: Showing model responses would provide a more concrete understanding of how the prompts work in practice and how the models generate citations based on the given instructions.
Implementation: For each type of prompt, include one or two examples of actual model responses, highlighting how the models follow the instructions, generate citations, and format their output. Choose examples that illustrate different aspects of the task, such as single-sentence answers, multi-sentence answers, and answers with varying levels of citation complexity.
The section could explicitly connect the prompts to the evaluation metrics used in the research, explaining how the prompts are designed to elicit responses that can be assessed using these metrics.
Rationale: Connecting prompts to evaluation metrics would strengthen the link between the evaluation methodology and the specific prompts used, providing a more comprehensive understanding of the evaluation process.
Implementation: For each type of prompt, briefly explain how the elicited responses are used to calculate the relevant evaluation metrics. For example, for correctness evaluation prompts, explain how the ratings provided by human evaluators or GPT-4o are used to calculate the correctness score. For citation quality evaluation prompts, explain how the responses are used to assess citation recall, precision, and F1 score.
Figure 13 shows an example of how the CoF pipeline teaches a language model to extract specific sentences that support a given statement. Imagine you have a long paragraph, and someone asks you a question about it. This figure shows how the model learns to pick out the exact sentences that answer the question. The figure includes a 'prompt,' which is like the instructions given to the model, and an 'output,' which is the model's response. The prompt gives the model a passage with numbered sentences and a statement. The model's task is to identify the sentence numbers that contain information supporting the statement. The output shows the model's response, which is a list of sentence numbers.
Text: "The prompt includes 3 examples and is shown in Figure 13."
Context: This sentence describes the prompt used for sentence-level citation extraction and refers to Figure 13 for a visual representation.
Relevance: This figure is important because it illustrates a crucial step in the CoF pipeline: teaching the model to pinpoint the exact sentences that provide evidence for a statement. This step ensures that the citations generated by the model are precise and directly support the answer, making it easier for users to verify the information.
Figure 4 shows the instructions given to GPT-4o, a powerful language model, to evaluate the quality of answers generated by another language model. It's like setting up a test for a student and providing clear guidelines for grading their answers. The prompt includes detailed instructions on what factors to consider when evaluating the answers, such as correctness, helpfulness, accuracy, and relevance. It also provides examples of answers and their corresponding ratings to guide GPT-4o in its assessment. The prompt emphasizes that GPT-4o should be an impartial judge and base its rating on the provided guidelines and examples.
Text: "The detailed prompts can be found in Figure 4, 5, and 6."
Context: This sentence refers to Figure 4, along with Figures 5 and 6, as examples of prompts used for evaluating the correctness of language model responses.
Relevance: This figure is important because it shows how the authors ensure a fair and consistent evaluation of the language models' answers. By providing clear instructions and examples to GPT-4o, they aim to minimize subjectivity and bias in the assessment process. This rigorous evaluation methodology is crucial for obtaining reliable results and comparing the performance of different models.
Figure 5 presents the prompt used to evaluate the correctness of AI assistant answers on the MultiFieldQA-zh/en, HotpotQA, and Dureader datasets. The prompt instructs an evaluator to rate the quality of an AI assistant's answer based on its correctness and comprehensiveness. The evaluator is asked to compare the AI's answer to a reference answer and provide an overall rating on a scale of 1 to 3. A rating of 1 indicates a wrong or irrelevant answer, 2 signifies a partially correct answer, and 3 represents a correct and comprehensive answer. The prompt emphasizes that correctness should be prioritized over comprehensiveness. The prompt also includes placeholders for the question, the reference answer, and the AI assistant's answer, which will be filled in with specific content during the evaluation process.
Text: "The detailed prompts can be found in Figure 4, 5, and 6."
Context: This sentence, found on page 4, refers to the prompts used for evaluating the correctness of AI assistant answers on different datasets. The authors are directing the reader to Figures 4, 5, and 6 for the specific content of these prompts.
Relevance: Figure 5 is relevant because it provides transparency into the evaluation process for answer correctness. By presenting the exact prompt used, the authors allow readers to understand how the quality of the AI assistant's answers was assessed. This transparency is crucial for replicating the study and for understanding the basis of the reported correctness scores.
Figure 6 displays the prompt used to assess the correctness of AI-generated summaries on the GovReport dataset. The prompt instructs an evaluator to rate the quality of a summary generated by an AI assistant, considering its correctness, comprehensiveness, and coherence. The evaluator is asked to compare the AI's summary to a reference summary and provide an overall rating on a scale of 1 to 5, with 1 being the lowest and 5 being the highest. The prompt emphasizes that correctness should be the primary consideration. It also includes placeholders for the question, the reference summary, and the AI assistant's summary, which will be filled in with specific content during the evaluation process.
Text: "The detailed prompts can be found in Figure 4, 5, and 6."
Context: This sentence, found on page 4, refers to the prompts used for evaluating the correctness of AI assistant answers on different datasets. The authors are directing the reader to Figures 4, 5, and 6 for the specific content of these prompts.
Relevance: Figure 6 is relevant because it provides transparency into the evaluation process for summary correctness on the GovReport dataset. By presenting the exact prompt used, the authors allow readers to understand how the quality of the AI-generated summaries was assessed. This transparency is crucial for replicating the study and for understanding the basis of the reported correctness scores for summaries.
Figure 7 presents the prompt used to evaluate whether a factual statement made by an AI assistant is supported by the cited snippet from a long document. The prompt instructs an expert evaluator to assess the level of support provided by the snippet, using a three-point scale: 'Fully supported,' 'Partially supported,' or 'No support.' It emphasizes that the evaluation should be based solely on the provided snippet, without using any external information or knowledge. The prompt includes placeholders for the user's question, the AI assistant's statement, and the concatenated cited snippet.
Text: "The prompts are shown in Figure 7 and 8."
Context: This sentence, found in Section 2.3.2, 'Evaluation of Citation Quality,' refers to Figures 7 and 8 as examples of prompts used for evaluating citation recall.
Relevance: This prompt is crucial for understanding how the authors evaluate the accuracy of citations generated by LLMs. It provides a structured framework for human evaluators to assess whether the cited text actually supports the AI assistant's statement. This evaluation is essential for measuring the faithfulness and reliability of the LLM's responses, as it ensures that the citations are not just randomly selected but actually provide evidence for the claims made.
Figure 10 presents a one-shot learning prompt used to teach a language model (LLM) the LAC-S strategy, which involves generating answers to user questions based on a long document and providing sentence-level citations. The prompt instructs the LLM to answer the question using information from the document and to include citations for any factual statements in its response. Citations are represented as a list of sentence numbers enclosed in square brackets and placed within '<cite>' tags. The prompt also specifies that sentences not requiring citations, such as introductory sentences or summaries, should still have empty '<cite>' tags to indicate that they don't need citations. The prompt includes an example to illustrate the desired format and content.
Text: "As illustrated in Figure 10, we number each sentence senti in the context by adding a prefix '<Ci>' and prompt the LLM with one demonstration."
Context: This sentence, found in Section 2.4, 'Benchmarking Results of Current Long-Context LLMs,' describes how the authors prepare the context and use a one-shot learning prompt (shown in Figure 10) to evaluate the LAC-S strategy.
Relevance: This prompt is crucial for understanding how the authors train LLMs to generate citations in the LAC-S strategy. It demonstrates the specific instructions and format used to teach the LLM how to identify factual statements, locate supporting sentences in the document, and include citations in its response. This prompt is essential for enabling the LLM to perform the LQAC task, where it needs to provide both accurate answers and precise citations to support its claims.
Figure 12 presents a prompt designed to guide a language model in adding citations to an existing answer. It instructs the model to identify factual statements within the answer and append the corresponding snippet numbers from the provided context. The prompt emphasizes preserving the original content and format of the answer while adding citations. It also includes an example to illustrate the desired output format, showing how to incorporate citations for factual statements and indicate the absence of citations for other types of sentences.
Text: "Figure 12 shows the prompt we use."
Context: The authors are describing the chunk-level citation generation step in their CoF pipeline and referring to Figure 12 for the specific prompt used in this step.
Relevance: This prompt is crucial for understanding how the CoF pipeline generates chunk-level citations. It provides a concrete example of the instructions given to the language model, highlighting the specific format and requirements for adding citations to an existing answer. This prompt is essential for the pipeline's ability to automatically create training data with accurate and consistent citations.
This brief section, found in Appendix D, states the approximate cost of evaluating model performance on the LongBench-Cite benchmark using GPT-4o. It indicates that evaluating correctness costs around $4 per run, while evaluating citation quality is more expensive, costing about $25 per run.
The section provides transparency by explicitly stating the approximate cost of using GPT-4o for evaluation, allowing readers to understand the financial implications of the chosen methodology.
While the section states the overall cost for correctness and citation quality evaluation, it would be more informative to provide a breakdown of these costs, explaining how they are calculated.
Rationale: A detailed breakdown would allow readers to understand the factors contributing to the evaluation cost and potentially explore ways to reduce these costs, such as by optimizing the evaluation process or using alternative evaluation methods.
Implementation: Explain how the costs are calculated, considering factors such as the number of queries, the length of the responses, the pricing model of GPT-4o, and any additional processing steps involved in the evaluation. Provide a breakdown of the costs for different aspects of the evaluation, such as evaluating correctness, recall, precision, and citation length.
The section could discuss the trade-off between evaluation cost and the quality of the evaluation, considering whether alternative methods might provide a more cost-effective solution without compromising the reliability of the assessment.
Rationale: Acknowledging the trade-off between cost and quality would provide a more balanced perspective on the evaluation methodology and encourage readers to consider alternative approaches, especially if cost is a significant constraint.
Implementation: Discuss the potential limitations of using less expensive evaluation methods, such as relying on smaller language models or using automated metrics that might not fully capture the nuances of human judgment. Explain the rationale for choosing GPT-4o despite its cost, emphasizing its advantages in terms of accuracy, reliability, and the ability to handle complex evaluation tasks.
The section could offer suggestions for reducing the cost of evaluation, such as using sampling techniques or exploring alternative evaluation methods.
Rationale: Providing strategies for cost reduction would be particularly helpful for researchers with limited budgets, allowing them to conduct evaluations without incurring prohibitive expenses.
Implementation: Suggest specific strategies for reducing evaluation costs, such as evaluating a subset of the benchmark data using sampling techniques, exploring the use of smaller or fine-tuned language models for evaluation, or developing automated metrics that are less computationally expensive but still provide reliable assessments. Discuss the potential trade-offs associated with each strategy, considering the impact on evaluation quality and the feasibility of implementation.