The Reliability of Large Language Models: A Comprehensive Analysis

Table of Contents

Overall Summary

Overview

This research investigates the reliability of large language models (LLMs) as they increase in size and undergo more advanced training methods. The study analyzes the performance of several LLM families (GPT, LLaMA, and BLOOM) on various benchmarks to understand how reliability changes with scale and training. Findings suggest that larger, more advanced models do not necessarily become more reliable, sometimes failing on simpler tasks, which necessitates a shift in AI development priorities.

Key Findings

Strengths

Areas for Improvement

Significant Elements

Figure 1

Description: Radar charts comparing key reliability indicators across LLM families, visualizing the trade-offs between correctness, difficulty concordance, and prompting stability.

Relevance: Summarizes core findings and highlights the potential negative impact of scaling and shaping on certain reliability aspects.

Figure 2

Description: Stacked bar charts showing the proportion of correct, avoidant, and incorrect responses for different LLMs across benchmarks and difficulty levels.

Relevance: Demonstrates the core findings regarding the relationship between model scale, shaping, and performance.

Conclusion

This study reveals a critical challenge in LLM development: scaling and shaping do not guarantee improved reliability. The findings highlight the need for a shift in AI development, prioritizing predictable error patterns and incorporating human factors into training processes. Further research should explore the causes of difficulty discordance, prompt sensitivity, and the trade-off between avoidance and incorrectness to develop more reliable and trustworthy LLMs.

Section Analysis

Abstract

Overview

This abstract summarizes a research study that investigates the reliability of large language models (LLMs) as they increase in size and incorporate more advanced training methods. The study finds that while larger, more sophisticated models often perform better on complex tasks, they do not necessarily become more reliable, sometimes failing on simpler tasks that humans and earlier models could handle. This highlights a need for a shift in AI development to prioritize predictable error patterns, especially in critical applications.

Key Aspects

Strengths

Suggestions for Improvement

Introduction

Overview

The introduction establishes the context of Large Language Model (LLM) development, highlighting the trend of scaling up (size, data, compute) and shaping up (fine-tuning, human feedback) to improve performance and alignment. However, it raises the concern that these advancements might compromise reliability, particularly regarding difficulty concordance, task avoidance, and prompting stability. The introduction emphasizes the need for a shift in AI development to prioritize predictable error patterns for reliable, real-world application.

Key Aspects

Strengths

Suggestions for Improvement

Non-Text Elements

Table Table 1

Table 1 provides a detailed comparison of various Large Language Models (LLMs) across three prominent families: GPT, LLaMA, and BLOOM. It lists model names, release years, scaling metrics (number of parameters, data tokens, and compute FLOPs), shaping instructions (e.g., FeedME, RLHF, S-FT), and alignment methods. The table shows the evolution of these models in terms of size, data, compute, and training strategies.

First Mention

Text: "Table 1 summarizes the details of models in these three families."

Context: This sentence appears in the second paragraph of the introduction, after discussing the scaling up and shaping up of LLMs and before introducing Figure 1.

Relevance: This table is crucial for understanding the context of the study. It provides a structured overview of the LLMs analyzed, allowing the reader to grasp the differences in scale, training methods, and development approaches across the model families. This information is essential for interpreting the subsequent analysis of model performance and reliability.

Critique
Visual Aspects
  • The table is well-organized, with clear column headers and row labels.
  • The use of abbreviations, while necessary due to space constraints, might require the reader to frequently refer to the footnotes for clarification.
  • The visual presentation could be enhanced by using color-coding or visual separators to distinguish between model families or different types of shaping instructions.
Analytical Aspects
  • The table effectively presents a large amount of information in a concise format.
  • The inclusion of scaling metrics (parameters, data, compute) allows for a quantitative comparison of model sizes and computational resources.
  • The table could benefit from a brief explanation of the key differences between the shaping instructions and alignment methods, as these are crucial for understanding the models' development and potential impact on reliability.
Numeric Data
  • Number of GPT Models: 10
  • Number of LLaMA Models: 10
  • Number of BLOOM Models: 12
  • GPT-3 ada Parameters: 350000000 parameters
  • BLOOM-176b Parameters: 176250000000 parameters

Methods

Overview

This section details the methodology employed in the research, including the selection of benchmarks, prompt templates, difficulty functions, response scoring, experimental setup, and model evaluation metrics. The researchers aim to provide a transparent and reproducible framework for analyzing LLM reliability.

Key Aspects

Strengths

Suggestions for Improvement

Results

Overview

This section presents the results of the study, focusing on the relationship between difficulty concordance, task avoidance, and prompting stability across different LLM families. The key finding is that while scaled-up, shaped-up models generally improve in correctness, they do not eliminate errors on easy instances and often trade avoidance for incorrect answers, raising concerns about reliability.

Key Aspects

Strengths

Suggestions for Improvement

Non-Text Elements

Figure Fig. 1

Figure 1 presents three radar charts comparing key indicators for several models in the GPT, LLaMA, and BLOOM families. These indicators include correctness proportion (c/(c+a+i)), difficulty concordance, prompting stability, prudence proportion ((c+a)/(c+a+i)), and prudence difficulty concordance. The charts distinguish between raw models (yellow to orange) and shaped-up models (light to dark blue). The shaped-up models generally show higher correctness and prompting stability but lower difficulty concordance and prudence.

First Mention

Text: "Figure 1 represents how some key indicators show that the shaped-up models (in blue) are more stable to prompt variation and are more correct, at the cost of being less concordant with human difficulty, and having more overall failures (less prudent)."

Context: This sentence appears in the second paragraph of the Results section, after introducing the three LLM families and the five selected benchmarks.

Relevance: Figure 1 visually summarizes the core findings of the study, highlighting the trade-offs between correctness, prompting stability, difficulty concordance, and prudence across different LLM families and model versions. It supports the central argument that scaling up and shaping up models may not lead to improved reliability in all aspects.

Critique
Visual Aspects
  • The radar chart format effectively compares multiple indicators simultaneously.
  • The color-coding helps distinguish between raw and shaped-up models, but some overlapping lines might make it difficult to compare individual models within a family.
  • The chart could benefit from clearer labels for the axes and indicators.
Analytical Aspects
  • The indicators provide a comprehensive overview of model reliability, considering both correctness and avoidance.
  • The aggregation of results from five benchmarks provides a general overview, but the individual benchmark results might reveal more nuanced insights.
  • The lack of specific numerical values on the radar charts makes it difficult to quantify the differences between models.
Numeric Data
  • Correctness Proportion (GPT-4 v.2): 90 %
  • Prudence Proportion (GPT-4 v.2): 60 %
  • Prompting Stability (GPT-4 v.2): 95 %
  • Difficulty Concordance (GPT-4 v.2): 40 %
  • Correctness Proportion (LLaMA-2-70b-chat): 85 %
Table Table 2

Table 2 describes the five benchmarks used in the study: Addition, Anagram, Locality, Science, and Transforms. For each benchmark, it provides examples, the chosen difficulty metric (and its abbreviation), and calibrated difficulty values for the given examples. The difficulty metrics are: fcry (number of carrying operations) for Addition, flet (number of letters) for Anagram, fpop (inverse of city popularity) for Locality, fhum (anticipated human difficulty) for Science, and fw+l (combination of word counts and Levenshtein distance) for Transforms. Calibrated difficulty values range from approximately 18 to 99.

First Mention

Text: "Table 2 provides an overview of the five benchmarks, the intrinsic difficulty function used as a proxy for human difficulty (discussed in the Methods), some examples and the calibrated human difficulty values for the given examples."

Context: This sentence appears towards the end of the first paragraph in the Results section, after discussing the difficulty proxies and the need for controlling human difficulty.

Relevance: Table 2 is essential for understanding the experimental design and how human difficulty was operationalized in the study. It provides context for interpreting the results presented in subsequent figures and analyses.

Critique
Visual Aspects
  • The table is clear and concise, with well-defined columns and examples.
  • The inclusion of calibrated difficulty values for the examples helps illustrate the difficulty metrics.
  • The table could benefit from a brief explanation of how the calibrated difficulty values were obtained.
Analytical Aspects
  • The chosen difficulty metrics seem reasonable for the respective benchmarks, although their correlation with actual human difficulty needs further validation.
  • The examples provided are illustrative, but a larger sample of examples would provide a better understanding of the benchmarks' scope and diversity.
  • The use of calibrated difficulty values allows for comparison across benchmarks, but the normalization process and its potential limitations should be discussed.
Numeric Data
  • Addition Calibrated Difficulty (Example 1): 35.25
  • Anagram Calibrated Difficulty (Example 1): 18.42
  • Locality Calibrated Difficulty (Example 1): 91.66
  • Science Calibrated Difficulty (Example 1): 37.02
  • Transforms Calibrated Difficulty (Example 1): 39.49
Figure Fig. 2

Figure 2 presents the performance of selected GPT and LLaMA models on five benchmarks (addition, anagram, locality, science, transforms) across varying difficulty levels. The figure uses stacked bar charts to show the proportion of correct, avoidant, and incorrect responses for each model and benchmark combination. The x-axis represents the calibrated human difficulty, and the y-axis represents the proportion of each response type. The figure highlights the increase in correct responses and the decrease in avoidance with scaled-up, shaped-up models.

First Mention

Text: "Figure 2 shows the results of a selection of models in the GPT and LLaMA families, increasingly scaled up, with the shaped-up models on the right, for the five domains: 'addition', 'anagram', 'locality', 'science' and 'transforms'."

Context: This sentence is the first sentence of the second paragraph in the Results section, immediately following the introductory paragraph.

Relevance: Figure 2 visually demonstrates the core findings regarding the relationship between model scale, shaping, and performance across different tasks and difficulty levels. It supports the observation that while correctness generally increases with scale and shaping, avoidance decreases, and incorrectness becomes more prevalent.

Critique
Visual Aspects
  • The stacked bar chart format clearly shows the proportion of each response type.
  • The use of color effectively distinguishes between correct, avoidant, and incorrect responses.
  • The x-axis labels could be clearer in indicating the difficulty ranges for each benchmark.
Analytical Aspects
  • The figure effectively demonstrates the trend of increasing correctness with model scale and shaping.
  • The decrease in avoidance and the corresponding increase in incorrectness are clearly visible.
  • The figure could benefit from statistical analysis to quantify the differences between models and the significance of the observed trends.
Numeric Data
  • Maximum Difficulty (Addition): 100
  • Minimum Difficulty (Addition): 22.8
  • Maximum Difficulty (Anagram): 99
  • Minimum Difficulty (Anagram): 19.2
  • Maximum Difficulty (Locality): 100
Figure Fig. 3

Figure 3, titled 'Evolution of types of supervision error versus difficulty according to human survey S2,' presents four line graphs in a 2x2 grid. Each graph depicts the relationship between difficulty (x-axis) and the proportion of different supervision error types (y-axis) for a specific benchmark (Addition, Anagram, Locality, Science, Transforms). The error types are 'Incorrect to avoidance,' 'Incorrect to correct,' 'Incorrect to incorrect,' and 'Incorrect to unsure.' Difficulty is presented in equal-sized bins. The graphs show how the proportion of each error type changes as the difficulty increases. The figure aims to illustrate the areas where the 'incorrect to correct' error (where participants mistakenly classify incorrect model outputs as correct) is low enough to be considered a safe operating region.

First Mention

Text: "With a three-valued confusion matrix with correctness, avoidance and incorrectness, we can focus on the frequency of non-avoidant cases for which humans believe the output is correct but it is not (Fig. 3)."

Context: This sentence appears towards the end of the Results section, after discussing the human studies S1 and S2 and before introducing the three core elements affecting LLM reliability.

Relevance: Figure 3 directly addresses the issue of human supervision errors, a critical aspect of LLM reliability. It shows how human ability to identify incorrect model outputs varies with task difficulty, highlighting the challenges in relying on human oversight for quality control.

Critique
Visual Aspects
  • The line graphs effectively show the trends of different error types across difficulty levels.
  • The use of a 2x2 grid allows for clear comparison between benchmarks.
  • The x-axis labels could be more informative by indicating the actual difficulty ranges within each bin.
Analytical Aspects
  • The 'incorrect to correct' error is a crucial metric for evaluating the reliability of LLMs in real-world scenarios where human supervision is involved.
  • The figure highlights the lack of consistent 'safe operating regions' across different benchmarks, indicating the difficulty in establishing reliable performance thresholds.
  • Further analysis could explore the reasons behind the different error patterns observed across benchmarks and investigate strategies for reducing human supervision errors.
Numeric Data
  • Number of Error Types: 4
  • Number of Benchmarks: 5
  • Difficulty Range (Addition): 22.75
  • Difficulty Range (Addition): 100
  • Difficulty Range (Anagram): 18.41
Figure Fig. 4

Figure 4, titled 'Scaling analysis of LLAMA and BLOOM families and non-instruct GPT models,' comprises three scatter plots exploring the relationship between FLOPs (floating-point operations, on a logarithmic scale) and model performance. The plots analyze avoidance (a), incorrectness (i), and ultracrepidarianism (i/(a+i)), which is the proportion of incorrect answers among non-correct responses. Different markers and colors represent the LLaMA, BLOOM, and non-instruct GPT model families. The figure aims to demonstrate how these metrics change with increasing model scale (FLOPs).

First Mention

Text: "With our data and three-outcome labelling, we can now analyse the unexplored evolution of avoidance and incorrectness (Fig. 4, left)."

Context: This sentence appears in the latter half of the Results section, after discussing the prompt sensitivity analysis and before summarizing the key findings.

Relevance: Figure 4 directly addresses the research question of how scaling affects LLM reliability. It provides a visual representation of the relationship between model size (FLOPs) and key metrics like avoidance, incorrectness, and ultracrepidarianism, allowing for an analysis of scaling trends across different model families.

Critique
Visual Aspects
  • The use of scatter plots with logarithmic scales is appropriate for visualizing the relationship between FLOPs and performance metrics.
  • The markers and colors effectively distinguish between different model families.
  • The labels and captions are clear and informative.
Analytical Aspects
  • The analysis of ultracrepidarianism provides a novel perspective on the tendency of larger models to provide incorrect answers rather than avoiding the question.
  • The figure reveals that while correctness generally increases with scale, incorrectness does not necessarily decrease, and ultracrepidarianism can even increase.
  • Further analysis could investigate the factors contributing to these trends and explore alternative scaling strategies that prioritize reliability.
Numeric Data
  • Number of Scatter Plots: 3
  • X-axis Scale: Logarithmic FLOPs
  • Metrics Analyzed: Avoidance, Incorrectness, Ultracrepidarianism
  • Model Families: LLaMA, BLOOM, non-instruct GPT
  • Purpose: Analyze scaling trends
Figure Extended Data Fig. 1

Extended Data Figure 1, titled 'Performance of GPT models over difficulty,' presents a series of grouped bar charts illustrating the performance of various GPT models across different tasks (addition, anagram, locality, science, transforms) and difficulty levels. Each bar chart represents a specific model and task combination, showing the proportion of incorrect (red), avoidant (light blue/teal), and correct (dark blue) responses. The x-axis represents difficulty, binned into intervals, while the y-axis represents the proportion of each response type. The figure aims to show how the performance of GPT models changes with increasing difficulty across different tasks.

First Mention

Text: "This is an expected result and holds consistently for the rest of the models, shown in Extended Data Fig. 1 (GPT), Extended Data Fig. 2 (LLaMA) and Supplementary Fig. 14 (BLOOM family)."

Context: This sentence appears early in the Results section, after presenting Figure 2 and discussing the general trend of increasing correct responses with scaled-up, shaped-up models.

Relevance: Extended Data Figure 1 provides a more comprehensive view of GPT model performance across different tasks and difficulty levels, supporting the general observation that correctness increases with model scale but that difficulty discordance persists. It complements Figure 2 by showing the detailed performance breakdown for all GPT models.

Critique
Visual Aspects
  • The grouped bar charts clearly show the proportion of each response type for each model and task.
  • The color scheme effectively distinguishes between response types.
  • The x-axis labels could be improved by showing the actual difficulty ranges within each bin.
Analytical Aspects
  • The figure confirms the trend of increasing correctness with model scale, but also highlights the persistent difficulty discordance, where even advanced models struggle with seemingly easy instances.
  • The figure could benefit from a more detailed analysis of the avoidance behavior, exploring the different types of avoidance and their relationship with difficulty.
  • Comparing the performance of GPT models with other families (LLaMA, BLOOM) would provide a more complete picture of the reliability landscape.
Numeric Data
  • Number of GPT Models: 10
  • Number of Benchmarks: 5
  • Response Types: Correct, Avoidant, Incorrect
  • Difficulty Representation: Binned Intervals
  • Purpose: Show performance variation with difficulty
Figure Extended Data Fig. 2

This figure presents the performance of various LLaMA models across five benchmarks: 'addition', 'anagram', 'locality', 'science', and 'transforms'. Each benchmark is represented by a row of plots, and each column represents a different LLaMA model (7b, 13b, 33b, 65b, 2-7b, 2-13b, 2-70b, 2-7b-chat, 2-13b-chat, 2-70b-chat). The x-axis of each plot represents the difficulty level, calibrated to human expectations (0-100). The y-axis represents the proportion of responses categorized as correct, avoidant, or incorrect. The plots use stacked bars to show the distribution of these response types for each model at different difficulty levels. For 'science', transparent yellow bars indicate a 25% random guess probability. Example difficulty values shown include 22.8, 98.7, and 100 for 'addition'; 19.2, 74.3, and 99 for 'anagram'; 91.7, 91.8, and 100 for 'locality'; 16.9, 51.7, and 100 for 'science'; and 40.3, 42.1, and 99.1 for 'transforms'.

First Mention

Text: "Plots for all GPT and LLaMA models are provided in Extended Data Figs. 1 and 2 and for the BLOOM family in Supplementary Fig. 14."

Context: This sentence appears at the end of the caption for Figure 2, which discusses the performance of selected GPT and LLaMA models with increasing difficulty.

Relevance: This figure provides a comprehensive overview of the performance of the LLaMA model family across different tasks and difficulty levels. It helps visualize the impact of scaling on model performance and the distribution of correct, avoidant, and incorrect responses. This is directly relevant to the paper's focus on analyzing the reliability of increasingly larger and more complex LLMs.

Critique
Visual Aspects
  • The grid layout effectively facilitates comparison across models and benchmarks.
  • The color-coding for correct, avoidant, and incorrect responses is clear and consistent.
  • The x-axis labels could be improved by providing more context or explanation of the difficulty metric.
Analytical Aspects
  • The figure clearly shows the trend of increasing correctness with larger model sizes.
  • The visualization of avoidant and incorrect responses provides insights into the models' behavior at different difficulty levels.
  • The figure could benefit from additional analysis or discussion of the observed patterns, such as the relationship between avoidance and incorrectness across difficulty levels.
Numeric Data
  • Number of Benchmarks: 5
  • Number of LLaMA Models: 10
  • Difficulty Range (Addition): 22.8
  • Difficulty Range (Anagram): 19.2
  • Difficulty Range (Locality): 91.7
Figure Extended Data Fig. 3

This figure illustrates the prompting stability of GPT models across five benchmarks ('addition', 'anagram', 'locality', 'science', 'transforms') and two response types (correctness and avoidance). Each plot in the 5x2 grid represents a specific benchmark and response type combination for a selection of GPT models (GPT-3 ada, GPT-3 davinci, text-davinci-003, GPT-3.5-turbo, and GPT-4 v2). The x-axis represents difficulty, and the y-axis represents the proportion of correct or avoidant responses. Grey curves represent the performance of 15 different prompt templates, while green and bronze curves highlight the best and worst-performing templates, respectively. Small green and bronze numbers within each plot correspond to template codes.

First Mention

Text: "Extended Data Figs. 3 and 4 for the most representative models of the GPT and LLaMA families, respectively"

Context: This phrase appears in the fifth paragraph of the Results section, within a discussion of prompt sensitivity and its relationship to difficulty.

Relevance: This figure directly addresses the research question of prompting stability, showing how sensitive different GPT models are to variations in prompt phrasing across different tasks and difficulty levels. It supports the finding that while shaped-up models are generally more stable, pockets of variability persist.

Critique
Visual Aspects
  • The use of grey curves for the majority of prompt templates and highlighting the best and worst performers in green and bronze is effective for visualizing the range of performance.
  • The plots could be improved by adding labels to the x and y axes, making them more self-explanatory.
  • The large number of overlapping grey lines can make it difficult to distinguish individual prompt performance.
Analytical Aspects
  • The figure provides a detailed view of prompt sensitivity across different models and tasks.
  • The comparison between raw and shaped-up models highlights the impact of shaping techniques on prompting stability.
  • Further analysis could quantify the variability in prompt performance and explore the factors contributing to this variability.
Numeric Data
  • Number of Benchmarks: 5
  • Number of Response Types: 2
  • Number of Prompt Templates: 15
Figure Extended Data Fig. 4

This figure examines the prompting stability of LLaMA models across five benchmarks ('addition', 'anagram', 'locality', 'science', 'transforms') for correctness and avoidance. Each plot in the grid represents a benchmark and response type combination for selected LLaMA models (7b, 65b, 2-70b, 2-13b-chat, 2-70b-chat). The x-axis represents difficulty, and the y-axis represents the proportion of correct or avoidant responses. Grey curves depict the performance of 15 prompt templates, with green and bronze curves highlighting the best and worst performers. Small numbers in green and bronze indicate template codes.

First Mention

Text: "Extended Data Figs. 3 and 4 for the most representative models of the GPT and LLaMA families, respectively"

Context: This phrase, found in the fifth paragraph of the Results section, refers to the figures illustrating prompt stability over difficulty for GPT and LLaMA models.

Relevance: This figure directly relates to the paper's investigation of prompting stability, showing how LLaMA models' performance varies with different prompt phrasings across tasks and difficulty levels. It complements the analysis of GPT models in Extended Data Fig. 3 and contributes to the overall understanding of how prompt sensitivity evolves with model scale and shaping.

Critique
Visual Aspects
  • The consistent use of grey, green, and bronze curves for prompt templates, best performers, and worst performers, respectively, maintains visual consistency with Extended Data Fig. 3.
  • The plots could benefit from clearer axis labels and a legend to improve readability.
  • The density of lines in some plots can make it challenging to distinguish individual prompt performance.
Analytical Aspects
  • The figure provides a detailed visualization of prompt sensitivity for LLaMA models.
  • The comparison across different LLaMA model sizes allows for an analysis of how prompting stability changes with scale.
  • Further analysis could quantify the variability in prompt performance and investigate the factors influencing this variability.
Numeric Data
  • Number of Benchmarks: 5
  • Number of Response Types: 2
  • Number of Prompt Templates: 15
Table Extended Data Table 1

Extended Data Table 1 presents a comprehensive comparison of various language models across the GPT, LLaMA, and BLOOM families, focusing on their performance in terms of correctness, prudence (correctness + avoidance), difficulty concordance, and prompting stability. The table provides numerical values for each metric, ranging from 0 to 100, with higher values indicating better performance. The data are further visualized in Figure 1.

First Mention

Text: "Extended Data Table 1 provides a more detailed perspective on the same results."

Context: This sentence appears at the end of the first paragraph of the Results section, following the discussion of Figure 1 and its key indicators.

Relevance: This table provides a detailed numerical breakdown of the performance metrics visualized in Figure 1, allowing for a more precise comparison of the models across different families and versions. It supports the main findings of the section by quantifying the observed trends in correctness, prudence, difficulty concordance, and prompting stability.

Critique
Visual Aspects
  • The table is well-organized, with clear column headers and row labels that identify the models and metrics.
  • The use of abbreviations (c, a, i) for correct, avoidant, and incorrect responses requires the reader to refer back to the text for clarification, which could be improved by including a brief explanation in the table caption or a separate legend.
  • The visual presentation could be enhanced by using color-coding or visual separators to distinguish between model families or different types of shaping instructions.
Analytical Aspects
  • The table provides a valuable quantitative complement to the visual representation in Figure 1, allowing for a more precise comparison of the models.
  • The inclusion of both correctness and prudence metrics provides a more nuanced understanding of model performance, considering both accurate responses and the ability to avoid incorrect answers.
  • The table could benefit from including additional information about the benchmarks used to calculate these metrics, such as the number of instances per benchmark and the distribution of difficulty levels.
Numeric Data
  • GPT-3 ada Proportion c/(c+a+i): 24.67
  • GPT-3 ada Difficulty Concordance (Correctness): 61.21
  • GPT-3 ada Prompting Stability (Correctness): 52.43
  • GPT-3 ada Proportion (c+a)/(c+a+i): 81.46
  • GPT-3 ada Difficulty Concordance (Prudence): 17.22
  • GPT-3 ada Prompting Stability (Prudence): 13.82

Discussion

Overview

This section discusses the implications of the study's findings, highlighting the trade-off between correctness and avoidance in scaled-up, shaped-up LLMs. It emphasizes the need for a shift in AI development, focusing on incorporating human difficulty expectations and output supervision into training and shaping processes. The discussion also addresses limitations of the study and suggests future research directions.

Key Aspects

Strengths

Suggestions for Improvement

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