Enhancing Trustworthiness in Long-Context Large Language Models through Citation Generation

Table of Contents

Overall Summary

Overview

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.

Key Findings

Strengths

Areas for Improvement

Significant Elements

figure

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.

table

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.

Conclusion

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.

Section Analysis

Abstract

Overview

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.

Key Aspects

Strengths

Suggestions for Improvement

Introduction

Overview

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.

Key Aspects

Strengths

Suggestions for Improvement

LongBench-Cite: Benchmark Long-Context QA with Citations

Overview

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.

Key Aspects

Strengths

Suggestions for Improvement

Non-Text Elements

figure 1

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.

First Mention

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.

Critique
Visual Aspects
  • The figure effectively uses a simple visual representation to convey the difference between the two citation methods.
  • The use of emoticons adds a touch of humor and makes the figure more engaging.
  • The font size is a bit small, making it slightly difficult to read the text in the panels.
Analytical Aspects
  • The figure could benefit from a more detailed caption that explicitly explains the connection between incomplete sentences and user experience.
  • Including an example where a chunk-level citation leads to a factually incorrect or misleading answer would further strengthen the argument for sentence-level citations.
  • The figure focuses on the user experience aspect but could also briefly mention the implications for accuracy and verifiability.
Numeric Data
table 1

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.

First Mention

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.

Critique
Visual Aspects
  • The table is well-organized and easy to read.
  • The column headings are clear and informative.
  • The use of different units for average length (words for English, characters for Chinese) could be confusing for some readers.
Analytical Aspects
  • The table could benefit from a brief explanation of why these specific datasets were chosen and how they represent different challenges in long-context question answering.
  • Including information about the average number of sentences per context would be helpful for understanding the granularity of the citation task.
  • The table focuses on quantitative data but could also include a brief qualitative description of each dataset, highlighting its unique characteristics or challenges.
Numeric Data
  • Number of data points in MultiFieldQA-en: 4559
  • Number of data points in MultiFieldQA-zh: 6701
  • Number of data points in HotpotQA: 9151
  • Number of data points in Dureader: 15768
  • Number of data points in GovReport: 8734
  • Number of data points in LongBench-Chat: 35571
  • Average length of contexts in MultiFieldQA-en: 150 words
  • Average length of contexts in MultiFieldQA-zh: 200 characters
  • Average length of contexts in HotpotQA: 200 words
  • Average length of contexts in Dureader: 200 characters
  • Average length of contexts in GovReport: 200 words
  • Average length of contexts in LongBench-Chat: 50 words
Table 2

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.

First Mention

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.

Critique
Visual Aspects
  • The table is well-organized and easy to read, with clear labels for each model, dataset, and metric.
  • The use of bold and underlined text effectively highlights the best and second-best performing models, making it easy to identify the top contenders.
  • The table could benefit from a brief caption explaining the meaning of each metric (recall, precision, F1 score, citation length) for readers unfamiliar with these concepts.
Analytical Aspects
  • The table clearly shows that open-source LLMs generally lag behind proprietary models in terms of citation quality, indicating a need for improvement in this area.
  • The results also reveal that even proprietary models have room for improvement, as their citation F1 scores are not particularly high, and their citation lengths suggest a coarse granularity.
  • The table provides valuable insights into the challenges of LQAC and motivates the need for more effective methods to enhance the citation generation capabilities of LLMs.
Numeric Data
  • Citation F1 Score (GPT-4o on LongBench-Chat): 65.6 %
  • Citation Length (GPT-4o on LongBench-Chat): 220 tokens
  • Citation F1 Score (LongCite-8B on LongBench-Chat): 72.0 %
  • Citation Length (LongCite-8B on LongBench-Chat): 85 tokens
Table 3

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.

First Mention

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.

Critique
Visual Aspects
  • The table is well-structured and easy to navigate, with clear labels for each model, dataset, and metric.
  • The use of color-coding (green for improvement, red for degradation) effectively highlights the impact of citation generation on correctness, making it easy to identify trends.
  • The table could benefit from a brief caption explaining the meaning of the correctness ratio (CR) and how it is calculated for readers unfamiliar with this concept.
Analytical Aspects
  • The table shows that in many cases, generating responses and citations in one pass leads to a decrease in correctness (CR < 100%), suggesting that this approach can be challenging for LLMs.
  • However, the authors' trained models (LongCite-8B and LongCite-9B) consistently show improvement in correctness when trained with citation information (CR > 100%), indicating the effectiveness of their approach.
  • The table provides valuable evidence that training LLMs with citation information not only enhances their citation generation capabilities but also improves their overall accuracy in answering questions based on long contexts.
Numeric Data
  • Correctness Ratio (GPT-4o on LongBench-Chat): 88 %
  • Correctness Ratio (LongCite-8B on LongBench-Chat): 107 %
  • Correctness Ratio (LongCite-9B on LongBench-Chat): 109 %

CoF: Automatic SFT Data Construction for LQAC

Overview

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.

Key Aspects

Strengths

Suggestions for Improvement

Non-Text Elements

flow diagram 2

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.

First Mention

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.

Critique
Visual Aspects
  • The figure effectively uses separate panels to illustrate each step of the pipeline, making the process easy to follow.
  • The use of arrows and text annotations clearly shows the flow of information and the actions performed at each step.
  • The figure could benefit from a more visually appealing design. Using different colors or shapes for each panel could make it more engaging.
Analytical Aspects
  • The figure provides a good high-level overview of the CoF pipeline, but it could include more details about the specific techniques used at each step.
  • For example, the figure could mention the type of retriever used for chunk retrieval or the prompting strategy used for citation generation.
  • Adding a brief explanation of the rationale behind each step would further enhance the figure's value. For instance, why is it necessary to first generate chunk-level citations and then refine them to sentence-level citations?
Numeric Data
table 4

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.

First Mention

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.

Critique
Visual Aspects
  • The table is well-organized, with clear headings and labels for each metric and dataset.
  • The use of bold text to highlight the best performing method for each metric makes it easy to identify the top contenders.
  • The table could benefit from a more visually appealing design. Using different colors or shading to distinguish the one-pass and post-hoc methods could improve readability.
Analytical Aspects
  • The table provides a comprehensive comparison of different LQAC strategies, but it could benefit from a more detailed explanation of the abbreviations used for each method.
  • Including a brief discussion of the computational cost of each strategy would be helpful for understanding the practical implications of the results.
  • The table focuses on quantitative metrics, but it could also include a qualitative analysis of the strengths and weaknesses of each strategy, based on manual inspection of the generated answers and citations.
Numeric Data
  • Average Citation F1 (CoF): 65.8 %
  • Average Correctness Ratio (CoF): 100 %
  • Average Citation Length (CoF): 89 tokens

LongCite: Teach Long-Context LLMs to Generate Citations

Overview

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.

Key Aspects

Strengths

Suggestions for Improvement

Non-Text Elements

Table 5

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?).

First Mention

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.

Critique
Visual Aspects
  • The table is clear and easy to read, with well-defined headings and rows.
  • The use of abbreviations for the different training data types could be confusing for readers unfamiliar with the terminology. It would be helpful to include a brief explanation of each abbreviation in the table caption or a footnote.
  • The table could benefit from visual cues, such as bolding the best-performing model for each metric, to highlight the key findings.
Analytical Aspects
  • The table effectively shows that training with citation information is essential for good citation generation performance.
  • The table could benefit from a more detailed analysis of the results. For example, why does the model trained with post-RAC-S data perform so poorly? What specific challenges arise from using this data creation method?
  • The table focuses on quantitative metrics but could also include a qualitative analysis of the generated citations. For example, are the citations grammatically correct? Do they accurately reflect the content of the source text?
Numeric Data
  • Citation F1 Score (LongCite-9B with standard SFT): 63.6 %
  • Citation F1 Score (LongCite-9B without data filtering): 61.2 %
  • Citation F1 Score (LongCite-9B with post-RAC-S data): 50.1 %
Bar Graph 3

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.

First Mention

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.

Critique
Visual Aspects
  • The graph is simple and easy to understand, with clear labels for the axes and the different correctness ranges.
  • The use of error bars is helpful for showing the variation in citation F1 scores within each group.
  • The graph could be more visually appealing. Using different colors for each bar or adding a title that summarizes the key finding would make it more engaging.
Analytical Aspects
  • The graph clearly shows a positive correlation between answer correctness and citation quality, supporting the authors' claim.
  • The graph could benefit from a more detailed explanation of the citation F1 score. What does a specific F1 score mean in terms of citation quality? How is it calculated?
  • The graph focuses on the average F1 score, but it would be interesting to see the distribution of F1 scores within each correctness group. Are there any outliers? How does the variation in citation quality change across different correctness levels?
Numeric Data

Related Works

Overview

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.

Key Aspects

Strengths

Suggestions for Improvement

Conclusion

Overview

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.

Key Aspects

Strengths

Suggestions for Improvement

Model Cards

Overview

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.

Key Aspects

Strengths

Suggestions for Improvement

Non-Text Elements

Table 8

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.

First Mention

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.

Critique
Visual Aspects
  • The table is clear and easy to read, with distinct columns for each piece of information.
  • The use of consistent formatting makes it easy to scan and compare different models.
  • The table could benefit from a brief caption that explains what a context window is and why it's important for LLMs, especially for readers unfamiliar with these concepts.
Analytical Aspects
  • The table provides a good summary of the evaluated models, but it could include additional information that would be helpful for understanding their capabilities.
  • For example, the table could mention the number of parameters in each model, which is a common indicator of model complexity and capacity.
  • The table could also include a brief description of the training data used for each model, as this can significantly influence their performance on different tasks.
Numeric Data
  • Context Window Size (Claude-3-Sonnet): 200000 tokens
  • Context Window Size (GPT-4o): 128000 tokens
  • Context Window Size (GLM-4): 128000 tokens
  • Context Window Size (GLM-4-9B-chat): 128000 tokens
  • Context Window Size (Llama-3.1-8B-Instruct): 128000 tokens
  • Context Window Size (Llama-3.1-70B-Instruct): 128000 tokens
  • Context Window Size (Mistral-Large-Instruct): 128000 tokens

Case Study

Overview

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.

Key Aspects

Strengths

Suggestions for Improvement

Non-Text Elements

Table 9

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.

First Mention

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.

Critique
Visual Aspects
  • The table effectively uses color-coding (red for incorrect, green for correct) to highlight the differences in the models' responses, making it easy to see where LongSFT-9B goes wrong.
  • The table could benefit from a more visually distinct separation between the question, the models' responses, and the citations. Using different font sizes, colors, or borders could improve readability.
  • Including a brief caption summarizing the key takeaway from the case study would make it more accessible to readers who might not read the entire text.
Analytical Aspects
  • The case study provides a clear example of how hallucinations can occur in LLMs and how citation-based training can help to mitigate this problem.
  • The case study could be strengthened by providing more context about the source document from which the information is extracted. What type of document is it? How long is it? This would help readers understand the challenges involved in answering the question.
  • The case study focuses on a single example. Including additional examples of hallucinations and how LongCite-9B avoids them would further demonstrate the robustness of the approach.
Numeric Data
Table 10

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.

First Mention

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.

Critique
Visual Aspects
  • The use of color-coding (red for coarse, green for detailed) effectively highlights the differences in the summaries, making it easy to see how LongCite-9B provides more specific information.
  • The table could benefit from a clearer visual separation between the question, the models' responses, and the citations. Using different font sizes, colors, or borders could improve readability.
  • The table is quite long and text-heavy. Breaking it down into smaller, more focused sections with clear headings could make it easier to digest.
Analytical Aspects
  • The case study provides a good example of how LongCite-9B leverages citations to generate a more comprehensive summary.
  • The case study could be strengthened by providing more context about the government report being summarized. What is the report about? What are the key findings? This would help readers understand the significance of the differences in the summaries.
  • The case study could benefit from a more quantitative analysis of the summaries. For example, how many key points are mentioned in each summary? How much of the source text is covered by each summary? This would provide a more objective measure of the differences in comprehensiveness.
Numeric Data
Table 11

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.

First Mention

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.

Critique
Visual Aspects
  • While referred to as a 'Table,' the element is presented as a textual case study with example model outputs, not a traditional table with rows and columns.
  • The use of color-coding (red for coarse response, green for detailed response) effectively highlights the differences between the two models' outputs, making it easy to see the impact of citation-based training.
  • The presentation could be improved by visually separating the two models' responses, perhaps using side-by-side text boxes or different background colors, to enhance readability and comparison.
Analytical Aspects
  • The case study provides a clear example of how LongCite-9B utilizes information from various parts of the document, but it could benefit from a more detailed explanation of how the citation numbers guide this process.
  • The analysis could be strengthened by quantifying the difference in comprehensiveness between the two summaries, perhaps by counting the number of key points covered by each model or comparing their coverage of different sections of the document.
  • The case study focuses on the positive impact of citation-based training, but it could also briefly discuss any potential limitations or challenges, such as the risk of generating irrelevant or inaccurate citations.
Numeric Data

Prompts

Overview

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.

Key Aspects

Strengths

Suggestions for Improvement

Non-Text Elements

figure 13

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.

First Mention

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.

Critique
Visual Aspects
  • The figure clearly presents the prompt and the output, making it easy to understand the task and the model's response.
  • The use of numbered sentences in the passage helps to visualize how the model identifies specific sentences.
  • The figure could benefit from a more visually appealing design. Using different colors or fonts to distinguish the prompt, the passage, the statement, and the output could improve readability.
Analytical Aspects
  • The figure provides a good example of the sentence-level citation extraction task, but it could be strengthened by including multiple examples with varying levels of complexity.
  • The figure focuses on the 'what' of the task but could also explain the 'why' and 'how.' For example, why is it important to extract sentence-level citations? How does the model learn to identify the relevant sentences?
  • The figure could benefit from a more detailed caption that explains the purpose of this step in the CoF pipeline and its significance for generating accurate and verifiable citations.
Numeric Data
textual prompt 4

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.

First Mention

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.

Critique
Visual Aspects
  • The figure effectively presents the prompt in a clear and structured format, making it easy to read and understand.
  • The use of bold text for key instructions and headings improves readability and highlights important information.
  • The figure could benefit from a more visually appealing presentation. Using different colors or fonts to distinguish the instructions, examples, and the rating scale could make it more engaging.
Analytical Aspects
  • The prompt provides a comprehensive set of guidelines for evaluating answer quality, but it could be more explicit about how to handle specific types of errors or inconsistencies.
  • The prompt could benefit from a more detailed explanation of the rating scale. What distinguishes a '5' from a '6' or a '7'? Providing more specific criteria for each rating level would enhance the consistency and objectivity of the evaluation.
  • The prompt focuses on evaluating the answers themselves but could also include instructions for assessing the citations provided by the language model. How should GPT-4o evaluate the relevance, accuracy, and completeness of the citations?
Numeric Data
Figure 5

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.

First Mention

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.

Critique
Visual Aspects
  • The figure presents the prompt as plain text within a text box, which is functional but not visually engaging.
  • Using a different font or background color for the prompt could make it stand out more from the surrounding text.
  • Adding a visual element, such as an icon representing evaluation or a checkmark for correctness, could make the figure more memorable.
Analytical Aspects
  • The prompt is clear and concise, providing specific instructions and a well-defined rating scale.
  • The prompt could benefit from a brief explanation of what constitutes 'correctness' and 'comprehensiveness' in the context of these datasets. Providing examples of answers that would receive different ratings could enhance clarity.
  • The prompt emphasizes prioritizing correctness over comprehensiveness, but it could also mention the importance of considering relevance and avoiding extraneous information in the AI's answer.
Numeric Data
  • Minimum Rating: 1
  • Maximum Rating: 3
Figure 6

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.

First Mention

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.

Critique
Visual Aspects
  • Similar to Figure 5, the prompt is presented as plain text within a text box, which is functional but not visually engaging.
  • Using a different font or background color for the prompt could make it stand out more from the surrounding text.
  • Adding a visual element, such as an icon representing summarization or a star rating, could make the figure more visually appealing.
Analytical Aspects
  • The prompt is clear and concise, providing specific instructions and a well-defined rating scale.
  • The prompt could benefit from a brief explanation of what constitutes 'correctness', 'comprehensiveness', and 'coherence' in the context of summary evaluation. Providing examples of summaries that would receive different ratings could enhance clarity.
  • The prompt emphasizes prioritizing correctness, but it could also mention the importance of considering conciseness, relevance, and avoiding redundancy in the AI's summary.
Numeric Data
  • Minimum Rating: 1
  • Maximum Rating: 5
textual prompt 7

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.

First Mention

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.

Critique
Visual Aspects
  • The prompt is presented as a text box, clearly separating it from the surrounding text.
  • The use of bold text for the rating options ('Fully supported,' 'Partially supported,' 'No support') makes them stand out and easy to identify.
  • The prompt could benefit from a more visually appealing design. Using different colors or font styles to highlight key instructions or information could improve readability.
Analytical Aspects
  • The prompt provides clear instructions and a well-defined rating scale, making it easy for evaluators to understand the task and provide consistent assessments.
  • The prompt explicitly emphasizes the importance of relying solely on the provided snippet, which helps to minimize bias and ensure that the evaluation is focused on the cited text.
  • The prompt could be strengthened by providing examples of each rating category, illustrating what constitutes 'Fully supported,' 'Partially supported,' and 'No support.' This would further enhance consistency and reduce ambiguity in the evaluation process.
Numeric Data
textual prompt 10

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.

First Mention

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.

Critique
Visual Aspects
  • The prompt is presented as a text box, clearly distinguishing it from the surrounding text.
  • The use of different font styles (bold, italics) helps to highlight key instructions and information, making the prompt easier to read and understand.
  • The prompt could benefit from a more visually appealing design. Using different colors or highlighting to emphasize specific sections, such as the example or the citation format, could further improve readability.
Analytical Aspects
  • The prompt provides clear and detailed instructions, guiding the LLM on how to answer the question, identify factual statements, and include citations in the correct format.
  • The use of a one-shot learning approach, with a single example, is a common and effective technique for teaching LLMs new tasks.
  • The prompt could be strengthened by providing more diverse examples, covering different types of questions, answers, and citation scenarios. This would expose the LLM to a wider range of possibilities and potentially improve its ability to generalize to new situations.
Numeric Data
textual prompt 12

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.

First Mention

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.

Critique
Visual Aspects
  • The prompt is presented as a text box, which is appropriate for representing textual instructions.
  • The use of bold text for key phrases like 'Your task' and 'Here is an example' helps to visually structure the prompt and guide the reader's attention.
  • The prompt could benefit from a more visually appealing design. Using different colors or font styles for different parts of the prompt (e.g., instructions, example input, example output) could improve readability and make it more engaging.
Analytical Aspects
  • The prompt is clear and well-structured, providing explicit instructions and a concrete example to guide the language model.
  • The prompt could be more explicit about the criteria for identifying factual statements. What types of sentences require citations? How should the model handle ambiguous cases?
  • The prompt focuses on adding citations to an existing answer, but it doesn't explain how the answer itself is generated. Providing more context about the answer generation process would enhance the understanding of the overall pipeline.
Numeric Data

Evaluation Cost

Overview

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.

Key Aspects

Strengths

Suggestions for Improvement

↑ Back to Top