A Peek into Token Bias: Large Language Models Are Not Yet Genuine Reasoners

Bowen Jiang, Yangxinyu Xie, Zhuoqun Hao, Xiaomeng Wang, Tanwi Mallick, Weijie J. Su, Camillo J. Taylor, Dan Roth·June 16, 2024

Summary

This study investigates the reasoning capabilities of large language models (LLMs) by designing controlled synthetic datasets with logical fallacies, focusing on conjunction and syllogistic problems. The research finds that LLMs, like GPT-4, primarily rely on token bias for success, rather than genuine reasoning, raising concerns about their ability to generalize and reason independently. The study employs token perturbation and statistical hypothesis testing to assess the extent to which LLMs can reason without context bias. Results consistently show that LLMs struggle with logical reasoning tasks, with performance improvements mostly due to recognizing superficial patterns. The findings suggest a need for more transparent and robust evaluation methods to determine true reasoning abilities in these models.

Key findings

3

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the issue of token bias in large language models (LLMs) and their reasoning capabilities, specifically focusing on the influences of weak and strong hints in problem-solving scenarios related to logical fallacies like the conjunction fallacy and syllogistic fallacy . It explores how LLMs may rely on hint tokens to derive correct inferences, indicating a potential lack of genuine reasoning skills . The study introduces a framework involving synthetic data generation, token perturbation, and statistical hypothesis testing to evaluate reasoning abilities and token bias in LLMs . This problem of token bias in reasoning tasks is not entirely new, but the paper provides a unique approach to rigorously test and analyze the impact of token bias on LLMs' reasoning processes, offering insights into the challenges posed by cognitive biases in artificial intelligence systems .


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate several scientific hypotheses related to the reasoning capabilities of Large Language Models (LLMs) through experiments and statistical analysis . The hypotheses include:

  • Hypothesis 1: LLMs fail to reason against contextually misleading options in conjunction fallacy problems .
  • Hypothesis 2: Genuine reasoning LLMs should withstand surface-level alterations to the one-shot exemplar in problem statements .
  • Hypothesis 3: Genuine reasoning LLMs should withstand irrelevant alterations to name entities in problem statements .
  • Hypothesis 4: Investigates whether LLMs overfit to specific quantifiers in reasoning about sets and tests the robustness of their reasoning abilities .
  • Hypothesis 5: LLMs might be misled by reputable names irrelevant to the logical structure in problem statements .
  • Hypothesis 6: Genuine Reasoning LLMs should not rely on hint tokens to derive correct inferences, but the experiments indicate that LLMs still heavily rely on hints for ideal performance .

What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "A Peek into Token Bias: Large Language Models Are Not Yet Genuine Reasoners" introduces several new ideas, methods, and models in the field of analyzing biases in Large Language Models (LLMs) . Here are some key points from the paper:

  1. Experimental Hypotheses Testing: The paper presents a series of hypotheses testing different aspects of LLM behavior, such as reasoning against contextually misleading options, token bias towards specific names, reliance on hint tokens, and more .

  2. Prompting Methods: The study implements various prompting strategies to evaluate the hypotheses, including baseline prompting, zero-shot chain-of-thought, one-shot prompting, and few-shots prompting . These prompting techniques are used to assess the reasoning capabilities and biases of LLMs in different scenarios.

  3. Models and Datasets: The research experiment involves a variety of commercial and open-sourced LLMs, such as OpenAI's gpt-3.5-turbo, gpt-4-turbo, gpt-4o, Meta llama models, Anthropic claude models, and Mistral models . Additionally, synthetic datasets are generated using sources like occupational statistics, commonsense stories, CNN news stories, disease symptom pairs, celebrity names, and more to analyze token bias in LLMs .

  4. Related Work: The paper discusses related studies that analyze biases and cognitive fallacies in LLMs, such as studies on synthetic datasets to reveal discrimination patterns, experiments on college admissions biases, and research on various fallacy types in human psychology . These related works provide a broader context for understanding the biases in LLMs.

  5. Statistical Analysis: The study provides a statistical guarantee and quantitative analysis of token bias in LLMs, offering a systematic approach to evaluating and tuning biases in these language models . The research aims to delve into a more fine-grained level of analysis compared to existing studies, focusing on general prompting strategies for hypothesis validation or rejection.

Overall, the paper contributes valuable insights into the biases and reasoning behavior of Large Language Models through rigorous experimental testing, prompting methods, model selection, and dataset generation, shedding light on the challenges and opportunities in this area of research . The paper "A Peek into Token Bias: Large Language Models Are Not Yet Genuine Reasoners" introduces novel characteristics and advantages compared to previous methods in analyzing biases in Large Language Models (LLMs) . Here are some key points highlighting these aspects:

  1. Experimental Hypotheses Testing: The paper conducts rigorous hypothesis testing to evaluate various aspects of LLM behavior, such as reasoning against contextually misleading options, token bias towards specific names, reliance on hint tokens, and more . This systematic approach allows for a comprehensive analysis of biases in LLMs compared to previous studies.

  2. Prompting Methods: The study implements diverse prompting strategies, including zero-shot chain-of-thought, one-shot prompting, and few-shots prompting, to assess the reasoning capabilities and biases of LLMs in different scenarios . These prompting techniques offer a more nuanced understanding of how LLMs respond to different types of inputs and challenges.

  3. Statistical Analysis: The research provides a statistical guarantee and quantitative analysis of token bias in LLMs, offering a systematic approach to evaluating and tuning biases in these language models . This statistical analysis enhances the reliability and robustness of the findings compared to previous methods, ensuring a more accurate assessment of biases in LLMs.

  4. Model Selection and Dataset Generation: The paper experiments with a variety of commercial and open-sourced LLMs, such as OpenAI's gpt-3.5-turbo, gpt-4-turbo, Meta llama models, Anthropic claude models, and Mistral models, to provide a comprehensive study of biases in different models . Additionally, synthetic datasets are generated from various sources like occupational statistics, commonsense stories, CNN news stories, disease symptom pairs, and celebrity names to analyze token bias in LLMs .

  5. Related Work and Contextual Examples: The study discusses related research on biases and cognitive fallacies in LLMs, providing a broader context for understanding biases in these models . By utilizing in-context examples and perturbed problems, the paper offers a detailed analysis of how LLMs respond to different scenarios, enhancing the understanding of token bias and reasoning capabilities in these models.

Overall, the paper's innovative characteristics lie in its comprehensive experimental design, diverse prompting methods, robust statistical analysis, model selection diversity, and contextual examples, providing a significant advancement in the analysis of biases in Large Language Models compared to previous methods .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies have been conducted in the field of large language models (LLMs) and token bias. Noteworthy researchers in this area include Melanie Mitchell, David C Krakauer, Nasrin Mostafazadeh, Nathanael Chambers, Xiaodong He, Devi Parikh, and many others . These researchers have contributed to understanding the reasoning abilities of LLMs and the impact of token bias on their performance.

The key to the solution mentioned in the paper "A Peek into Token Bias: Large Language Models Are Not Yet Genuine Reasoners" is the development of a hypothesis-testing framework to assess whether LLMs possess genuine reasoning abilities or primarily rely on token bias . This framework goes beyond evaluating LLMs based on accuracy and aims to investigate their token bias in solving logical reasoning tasks. By carefully controlling synthetic datasets and hypotheses, the researchers were able to identify and analyze the extent to which LLMs rely on token bias rather than genuine reasoning in their decision-making processes.


How were the experiments in the paper designed?

The experiments in the paper were designed with a structured approach focusing on hypothesis testing and evaluation of reasoning capabilities of Large Language Models (LLMs) . The experiments involved creating synthetic datasets dynamically to preclude prior existence in training datasets and control dataset size for statistical validity . Different prompting methods were utilized, such as zero-shot, one-shot, and few-shots prompting strategies, to evaluate the null hypotheses within the framework . The experiments rigorously tested various hypotheses related to token bias, including conjunction fallacy, syllogistic fallacy, and reasoning against contextually misleading options . Additionally, the experiments included perturbing tokens to assess LLMs' performance on reasoning tasks and evaluating their reliance on hint tokens for solving logical fallacy problems . The study applied statistical hypothesis testing, such as the McNemar test, and the Benjamini-Hochberg Procedure to determine the acceptance or rejection of null hypotheses based on p-values .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the "Disease symptom description dataset" . The code used in the study is open source, as it mentions leveraging data sources such as occupational statistics, commonsense stories, CNN news stories, common disease symptom pairs, celebrity names, objects vocabularies, and common U.S. news media to curate lists of entities for generating synthetic data .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that need to be verified . The study rigorously tests the reasoning capabilities of Large Language Models (LLMs) through a series of hypotheses related to token bias and genuine reasoning. The experiments include various perturbations and prompt methods to evaluate how LLMs reason and make inferences in different scenarios.

The results of the experiments consistently reject the null hypotheses, indicating that LLMs exhibit specific biases and tendencies in reasoning tasks . For example, the experiments show that LLMs heavily rely on hint tokens for solving logical fallacy problems, highlighting a significant reliance on external cues for reasoning . Additionally, the study reveals that LLMs may be misled by irrelevant celebrity names and reputable names, impacting their logical structure analysis .

Through statistical hypothesis testing and quantitative analysis, the paper carefully evaluates token bias in LLMs and provides insights into their reasoning behavior . The experiments demonstrate how altering tokens systematically can affect the performance of LLMs in reasoning tasks, shedding light on the importance of token perturbation in understanding LLM behavior . Overall, the experiments and results offer valuable evidence to support the hypotheses and contribute to the understanding of LLM reasoning capabilities and biases.


What are the contributions of this paper?

The paper makes significant contributions by reconceptualizing the evaluation of Large Language Models (LLMs) in terms of token bias. It presents statistical evidence within a hypothesis-testing framework, indicating that LLMs do not consistently apply reasoning in their decision-making processes but primarily rely on token bias for generating responses . This challenges the notion that LLMs engage in genuine reasoning and suggests that approaches like chain-of-thought prompting or in-context learning may lead to semantic shortcuts rather than actual reasoning . The findings underscore the need for further research to understand the mechanisms and limitations of LLMs' reasoning capabilities .


What work can be continued in depth?

Further research in this area can delve deeper into analyzing cognitive biases and logical fallacies in Large Language Models (LLMs) . Specifically, exploring synthetic datasets to uncover patterns of discrimination, anchoring, framing, group attribution, and primacy bias in LLMs could be a valuable continuation . Additionally, studying a broader range of fallacy types beyond the conjunction fallacy and syllogistic fallacy, as well as expanding the hypotheses and assumptions that genuine reasoners should satisfy, would enhance the understanding of LLMs' reasoning capabilities . This deeper investigation could provide more insights into the token bias and reasoning abilities of LLMs, contributing to the advancement of research in this field.

Tables

7

Introduction
Background
Emergence of large language models (LLMs) and their increasing prevalence
Importance of reasoning in AI and its role in decision-making
Objective
To evaluate LLMs' reasoning capabilities, specifically in conjunction and syllogistic problems
To uncover reliance on token bias vs. genuine reasoning
Method
Data Collection
Design of controlled synthetic datasets with logical fallacies
Inclusion of conjunction and syllogistic tasks
Data Preprocessing
Ensuring balanced and diverse representation of logical structures
Removing irrelevant context to isolate reasoning tests
Token Perturbation
Manipulating input to test for context bias reliance
Varying the presence of key tokens in the problem statements
Statistical Hypothesis Testing
Employing statistical methods to analyze performance changes
Assessing the significance of observed improvements
Results and Analysis
Performance Evaluation
LLMs' initial performance on logical reasoning tasks
Token bias as the primary factor in success
Struggles with Logical Reasoning
Consistent underperformance in tasks requiring genuine reasoning
Surface pattern recognition as the main strength
Context Bias Impact
The extent to which LLMs rely on context for problem-solving
Decline in performance with reduced context
Implications and Discussion
Limitations of current LLM reasoning abilities
The need for more transparent evaluation methods
Future directions for model development and evaluation
Conclusion
Summary of findings and their significance for AI research
The importance of genuine reasoning in AI's advancement
Recommendations for improving LLMs' reasoning capabilities
Basic info
papers
computation and language
artificial intelligence
Advanced features
Insights
What are the main conclusions about LLMs' genuine reasoning abilities based on the study's results?
How do the researchers measure the reliance of LLMs like GPT-4 on token bias for success?
What type of dataset does the study use to examine the reasoning capabilities of LLMs?
What method does the study employ to test LLMs' reasoning without context bias?

A Peek into Token Bias: Large Language Models Are Not Yet Genuine Reasoners

Bowen Jiang, Yangxinyu Xie, Zhuoqun Hao, Xiaomeng Wang, Tanwi Mallick, Weijie J. Su, Camillo J. Taylor, Dan Roth·June 16, 2024

Summary

This study investigates the reasoning capabilities of large language models (LLMs) by designing controlled synthetic datasets with logical fallacies, focusing on conjunction and syllogistic problems. The research finds that LLMs, like GPT-4, primarily rely on token bias for success, rather than genuine reasoning, raising concerns about their ability to generalize and reason independently. The study employs token perturbation and statistical hypothesis testing to assess the extent to which LLMs can reason without context bias. Results consistently show that LLMs struggle with logical reasoning tasks, with performance improvements mostly due to recognizing superficial patterns. The findings suggest a need for more transparent and robust evaluation methods to determine true reasoning abilities in these models.
Mind map
Decline in performance with reduced context
The extent to which LLMs rely on context for problem-solving
Assessing the significance of observed improvements
Employing statistical methods to analyze performance changes
Varying the presence of key tokens in the problem statements
Manipulating input to test for context bias reliance
Context Bias Impact
Token bias as the primary factor in success
LLMs' initial performance on logical reasoning tasks
Statistical Hypothesis Testing
Token Perturbation
Inclusion of conjunction and syllogistic tasks
Design of controlled synthetic datasets with logical fallacies
To uncover reliance on token bias vs. genuine reasoning
To evaluate LLMs' reasoning capabilities, specifically in conjunction and syllogistic problems
Importance of reasoning in AI and its role in decision-making
Emergence of large language models (LLMs) and their increasing prevalence
Recommendations for improving LLMs' reasoning capabilities
The importance of genuine reasoning in AI's advancement
Summary of findings and their significance for AI research
Future directions for model development and evaluation
The need for more transparent evaluation methods
Limitations of current LLM reasoning abilities
Struggles with Logical Reasoning
Performance Evaluation
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Implications and Discussion
Results and Analysis
Method
Introduction
Outline
Introduction
Background
Emergence of large language models (LLMs) and their increasing prevalence
Importance of reasoning in AI and its role in decision-making
Objective
To evaluate LLMs' reasoning capabilities, specifically in conjunction and syllogistic problems
To uncover reliance on token bias vs. genuine reasoning
Method
Data Collection
Design of controlled synthetic datasets with logical fallacies
Inclusion of conjunction and syllogistic tasks
Data Preprocessing
Ensuring balanced and diverse representation of logical structures
Removing irrelevant context to isolate reasoning tests
Token Perturbation
Manipulating input to test for context bias reliance
Varying the presence of key tokens in the problem statements
Statistical Hypothesis Testing
Employing statistical methods to analyze performance changes
Assessing the significance of observed improvements
Results and Analysis
Performance Evaluation
LLMs' initial performance on logical reasoning tasks
Token bias as the primary factor in success
Struggles with Logical Reasoning
Consistent underperformance in tasks requiring genuine reasoning
Surface pattern recognition as the main strength
Context Bias Impact
The extent to which LLMs rely on context for problem-solving
Decline in performance with reduced context
Implications and Discussion
Limitations of current LLM reasoning abilities
The need for more transparent evaluation methods
Future directions for model development and evaluation
Conclusion
Summary of findings and their significance for AI research
The importance of genuine reasoning in AI's advancement
Recommendations for improving LLMs' reasoning capabilities
Key findings
3

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the issue of token bias in large language models (LLMs) and their reasoning capabilities, specifically focusing on the influences of weak and strong hints in problem-solving scenarios related to logical fallacies like the conjunction fallacy and syllogistic fallacy . It explores how LLMs may rely on hint tokens to derive correct inferences, indicating a potential lack of genuine reasoning skills . The study introduces a framework involving synthetic data generation, token perturbation, and statistical hypothesis testing to evaluate reasoning abilities and token bias in LLMs . This problem of token bias in reasoning tasks is not entirely new, but the paper provides a unique approach to rigorously test and analyze the impact of token bias on LLMs' reasoning processes, offering insights into the challenges posed by cognitive biases in artificial intelligence systems .


What scientific hypothesis does this paper seek to validate?

This paper seeks to validate several scientific hypotheses related to the reasoning capabilities of Large Language Models (LLMs) through experiments and statistical analysis . The hypotheses include:

  • Hypothesis 1: LLMs fail to reason against contextually misleading options in conjunction fallacy problems .
  • Hypothesis 2: Genuine reasoning LLMs should withstand surface-level alterations to the one-shot exemplar in problem statements .
  • Hypothesis 3: Genuine reasoning LLMs should withstand irrelevant alterations to name entities in problem statements .
  • Hypothesis 4: Investigates whether LLMs overfit to specific quantifiers in reasoning about sets and tests the robustness of their reasoning abilities .
  • Hypothesis 5: LLMs might be misled by reputable names irrelevant to the logical structure in problem statements .
  • Hypothesis 6: Genuine Reasoning LLMs should not rely on hint tokens to derive correct inferences, but the experiments indicate that LLMs still heavily rely on hints for ideal performance .

What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "A Peek into Token Bias: Large Language Models Are Not Yet Genuine Reasoners" introduces several new ideas, methods, and models in the field of analyzing biases in Large Language Models (LLMs) . Here are some key points from the paper:

  1. Experimental Hypotheses Testing: The paper presents a series of hypotheses testing different aspects of LLM behavior, such as reasoning against contextually misleading options, token bias towards specific names, reliance on hint tokens, and more .

  2. Prompting Methods: The study implements various prompting strategies to evaluate the hypotheses, including baseline prompting, zero-shot chain-of-thought, one-shot prompting, and few-shots prompting . These prompting techniques are used to assess the reasoning capabilities and biases of LLMs in different scenarios.

  3. Models and Datasets: The research experiment involves a variety of commercial and open-sourced LLMs, such as OpenAI's gpt-3.5-turbo, gpt-4-turbo, gpt-4o, Meta llama models, Anthropic claude models, and Mistral models . Additionally, synthetic datasets are generated using sources like occupational statistics, commonsense stories, CNN news stories, disease symptom pairs, celebrity names, and more to analyze token bias in LLMs .

  4. Related Work: The paper discusses related studies that analyze biases and cognitive fallacies in LLMs, such as studies on synthetic datasets to reveal discrimination patterns, experiments on college admissions biases, and research on various fallacy types in human psychology . These related works provide a broader context for understanding the biases in LLMs.

  5. Statistical Analysis: The study provides a statistical guarantee and quantitative analysis of token bias in LLMs, offering a systematic approach to evaluating and tuning biases in these language models . The research aims to delve into a more fine-grained level of analysis compared to existing studies, focusing on general prompting strategies for hypothesis validation or rejection.

Overall, the paper contributes valuable insights into the biases and reasoning behavior of Large Language Models through rigorous experimental testing, prompting methods, model selection, and dataset generation, shedding light on the challenges and opportunities in this area of research . The paper "A Peek into Token Bias: Large Language Models Are Not Yet Genuine Reasoners" introduces novel characteristics and advantages compared to previous methods in analyzing biases in Large Language Models (LLMs) . Here are some key points highlighting these aspects:

  1. Experimental Hypotheses Testing: The paper conducts rigorous hypothesis testing to evaluate various aspects of LLM behavior, such as reasoning against contextually misleading options, token bias towards specific names, reliance on hint tokens, and more . This systematic approach allows for a comprehensive analysis of biases in LLMs compared to previous studies.

  2. Prompting Methods: The study implements diverse prompting strategies, including zero-shot chain-of-thought, one-shot prompting, and few-shots prompting, to assess the reasoning capabilities and biases of LLMs in different scenarios . These prompting techniques offer a more nuanced understanding of how LLMs respond to different types of inputs and challenges.

  3. Statistical Analysis: The research provides a statistical guarantee and quantitative analysis of token bias in LLMs, offering a systematic approach to evaluating and tuning biases in these language models . This statistical analysis enhances the reliability and robustness of the findings compared to previous methods, ensuring a more accurate assessment of biases in LLMs.

  4. Model Selection and Dataset Generation: The paper experiments with a variety of commercial and open-sourced LLMs, such as OpenAI's gpt-3.5-turbo, gpt-4-turbo, Meta llama models, Anthropic claude models, and Mistral models, to provide a comprehensive study of biases in different models . Additionally, synthetic datasets are generated from various sources like occupational statistics, commonsense stories, CNN news stories, disease symptom pairs, and celebrity names to analyze token bias in LLMs .

  5. Related Work and Contextual Examples: The study discusses related research on biases and cognitive fallacies in LLMs, providing a broader context for understanding biases in these models . By utilizing in-context examples and perturbed problems, the paper offers a detailed analysis of how LLMs respond to different scenarios, enhancing the understanding of token bias and reasoning capabilities in these models.

Overall, the paper's innovative characteristics lie in its comprehensive experimental design, diverse prompting methods, robust statistical analysis, model selection diversity, and contextual examples, providing a significant advancement in the analysis of biases in Large Language Models compared to previous methods .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies have been conducted in the field of large language models (LLMs) and token bias. Noteworthy researchers in this area include Melanie Mitchell, David C Krakauer, Nasrin Mostafazadeh, Nathanael Chambers, Xiaodong He, Devi Parikh, and many others . These researchers have contributed to understanding the reasoning abilities of LLMs and the impact of token bias on their performance.

The key to the solution mentioned in the paper "A Peek into Token Bias: Large Language Models Are Not Yet Genuine Reasoners" is the development of a hypothesis-testing framework to assess whether LLMs possess genuine reasoning abilities or primarily rely on token bias . This framework goes beyond evaluating LLMs based on accuracy and aims to investigate their token bias in solving logical reasoning tasks. By carefully controlling synthetic datasets and hypotheses, the researchers were able to identify and analyze the extent to which LLMs rely on token bias rather than genuine reasoning in their decision-making processes.


How were the experiments in the paper designed?

The experiments in the paper were designed with a structured approach focusing on hypothesis testing and evaluation of reasoning capabilities of Large Language Models (LLMs) . The experiments involved creating synthetic datasets dynamically to preclude prior existence in training datasets and control dataset size for statistical validity . Different prompting methods were utilized, such as zero-shot, one-shot, and few-shots prompting strategies, to evaluate the null hypotheses within the framework . The experiments rigorously tested various hypotheses related to token bias, including conjunction fallacy, syllogistic fallacy, and reasoning against contextually misleading options . Additionally, the experiments included perturbing tokens to assess LLMs' performance on reasoning tasks and evaluating their reliance on hint tokens for solving logical fallacy problems . The study applied statistical hypothesis testing, such as the McNemar test, and the Benjamini-Hochberg Procedure to determine the acceptance or rejection of null hypotheses based on p-values .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the "Disease symptom description dataset" . The code used in the study is open source, as it mentions leveraging data sources such as occupational statistics, commonsense stories, CNN news stories, common disease symptom pairs, celebrity names, objects vocabularies, and common U.S. news media to curate lists of entities for generating synthetic data .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide strong support for the scientific hypotheses that need to be verified . The study rigorously tests the reasoning capabilities of Large Language Models (LLMs) through a series of hypotheses related to token bias and genuine reasoning. The experiments include various perturbations and prompt methods to evaluate how LLMs reason and make inferences in different scenarios.

The results of the experiments consistently reject the null hypotheses, indicating that LLMs exhibit specific biases and tendencies in reasoning tasks . For example, the experiments show that LLMs heavily rely on hint tokens for solving logical fallacy problems, highlighting a significant reliance on external cues for reasoning . Additionally, the study reveals that LLMs may be misled by irrelevant celebrity names and reputable names, impacting their logical structure analysis .

Through statistical hypothesis testing and quantitative analysis, the paper carefully evaluates token bias in LLMs and provides insights into their reasoning behavior . The experiments demonstrate how altering tokens systematically can affect the performance of LLMs in reasoning tasks, shedding light on the importance of token perturbation in understanding LLM behavior . Overall, the experiments and results offer valuable evidence to support the hypotheses and contribute to the understanding of LLM reasoning capabilities and biases.


What are the contributions of this paper?

The paper makes significant contributions by reconceptualizing the evaluation of Large Language Models (LLMs) in terms of token bias. It presents statistical evidence within a hypothesis-testing framework, indicating that LLMs do not consistently apply reasoning in their decision-making processes but primarily rely on token bias for generating responses . This challenges the notion that LLMs engage in genuine reasoning and suggests that approaches like chain-of-thought prompting or in-context learning may lead to semantic shortcuts rather than actual reasoning . The findings underscore the need for further research to understand the mechanisms and limitations of LLMs' reasoning capabilities .


What work can be continued in depth?

Further research in this area can delve deeper into analyzing cognitive biases and logical fallacies in Large Language Models (LLMs) . Specifically, exploring synthetic datasets to uncover patterns of discrimination, anchoring, framing, group attribution, and primacy bias in LLMs could be a valuable continuation . Additionally, studying a broader range of fallacy types beyond the conjunction fallacy and syllogistic fallacy, as well as expanding the hypotheses and assumptions that genuine reasoners should satisfy, would enhance the understanding of LLMs' reasoning capabilities . This deeper investigation could provide more insights into the token bias and reasoning abilities of LLMs, contributing to the advancement of research in this field.

Tables
7
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