Break the Chain: Large Language Models Can be Shortcut Reasoners

Mengru Ding, Hanmeng Liu, Zhizhang Fu, Jian Song, Wenbo Xie, Yue Zhang·June 04, 2024

Summary

This paper critically examines Chain-of-Thought (CoT) prompting in large language models, particularly its limitations in complex reasoning tasks. Researchers propose "break the chain" strategies that incorporate human-like heuristics to improve efficiency without compromising performance. The ShortcutQA dataset is introduced to test reasoning through shortcuts, revealing that smaller models can benefit from CoT in few-shot scenarios, while zero-shot models with shortcut reasoning often outperform or match CoT. As model size increases, "break the chain" strategies become more effective, with shortcut reasoning enhancing computational efficiency. The study also highlights the importance of understanding the relationship between prompt design and task nature, as well as the trade-offs between CoT and shortcut-based reasoning. The research contributes to the ongoing debate on the best approaches for enhancing AI problem-solving and reasoning capabilities.

Key findings

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Paper digest

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

The paper aims to investigate the resilience and limitations of Large Language Models (LLMs) in employing Chain-of-Thought (CoT) reasoning by introducing a novel experimental framework called "break the chain." This approach seeks to understand the conditions under which CoT reasoning may fail and provides insights into the reasoning capabilities of LLMs . The study explores the effectiveness, limitations, and mechanisms of CoT by comparing it with different prompts derived from the "break the chain" strategy in both few-shot and zero-shot scenarios, as well as pioneering the use of shortcut reasoning prompts to enhance problem-solving capabilities . The research introduces the ShortcutQA dataset to evaluate the ability of LLMs to employ heuristic shortcuts, aiming to minimize computational demands while maintaining or enhancing performance accuracy . The paper addresses the challenge of optimizing reasoning processes in LLMs by exploring the impact of disrupted CoT demonstrations, the effectiveness of shortcut reasoning prompts, and the nuanced interplay between prompt nature and LLM performance . This problem is not entirely new but represents a novel approach to evaluating and enhancing the reasoning capabilities of LLMs through the introduction of shortcut reasoning strategies and the examination of CoT limitations .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis that Large Language Models (LLMs) can effectively employ heuristic shortcuts, similar to human cognitive shortcuts, to efficiently solve complex problems . The study investigates the resilience and limitations of LLMs in employing Chain-of-Thought (CoT) reasoning by introducing experimental frameworks that disrupt the logical progression of thought to understand the conditions under which CoT reasoning may falter . Additionally, the research explores the impact of CoT disruption in both zero-shot and few-shot scenarios to evaluate the model's ability to maintain coherent and accurate reasoning despite the disordered presentation of steps .


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

The paper "Break the Chain: Large Language Models Can be Shortcut Reasoners" introduces innovative ideas, methods, and models to evaluate the reasoning capabilities of Large Language Models (LLMs) . One key concept is the "break the chain" strategy, which challenges the traditional Chain-of-Thought (CoT) reasoning by disrupting the logical progression of thought sequences . This approach aims to uncover the conditions under which CoT reasoning may fail, providing insights into the underlying mechanisms of LLMs' reasoning abilities .

The study explores the impact of token limits on model performance by experimenting with different contexts, such as zero-shot and few-shot scenarios, to understand the implications of CoT disruption . The research delves into the significance of stepwise logical progression in model reasoning efficacy and evaluates the model's ability to maintain coherent and accurate reasoning despite disturbances in the sequence of sentences .

Additionally, the paper introduces the ShortcutQA dataset, which focuses on questions requiring shortcut reasoning, a form of intuitive problem-solving that deviates from traditional step-by-step logical deduction . This dataset aims to test the hypothesis that LLMs can effectively utilize heuristic shortcuts similar to human cognitive shortcuts to efficiently solve complex problems .

Furthermore, the study documents experiments with models of varying sizes under CoT and "break the chain" conditions, highlighting that smaller models exhibit a more pronounced reliance on CoT, especially as the model size decreases . The findings challenge the assumption that CoT invariably enhances LLM performance, suggesting that specific prompts, even without detailed reasoning, can yield comparable or superior outcomes .

Overall, the paper presents a comprehensive evaluation of CoT reasoning in LLMs, emphasizing the limitations and potential enhancements through innovative strategies like the "break the chain" approach and the use of the ShortcutQA dataset . These new ideas and methods provide valuable insights into improving the problem-solving capabilities of AI systems in real-world applications . The paper "Break the Chain: Large Language Models Can be Shortcut Reasoners" introduces innovative characteristics and advantages compared to previous methods in evaluating Large Language Models (LLMs) .

  1. "Break the Chain" Strategy: The paper proposes the "break the chain" strategy, which challenges the traditional Chain-of-Thought (CoT) reasoning by disrupting the logical progression of thought sequences . This approach aims to uncover the conditions under which CoT reasoning may fail, providing insights into the underlying mechanisms of LLMs' reasoning abilities .

  2. Efficiency and Performance: Shortcut reasoning significantly reduces token consumption, offering a vital advantage in computational efficiency. It not only conserves resources but also consistently outperforms traditional CoT methods across various datasets, highlighting its robustness and scalability in enhancing LLM performance .

  3. ShortcutQA Dataset: The introduction of the ShortcutQA dataset focuses on questions requiring shortcut reasoning, deviating from traditional step-by-step logical deduction. This dataset challenges current LLMs and sets a benchmark for future enhancements, providing a robust platform for testing and refining next-generation models .

  4. Model Size Impact: The study documents experiments with models of varying sizes under CoT and "break the chain" conditions, revealing that smaller models exhibit a more pronounced reliance on CoT, especially as the model size decreases. This challenges the assumption that CoT invariably enhances LLM performance, suggesting that specific prompts, even without detailed reasoning, can yield comparable or superior outcomes .

  5. Experimental Framework: The research methodology juxtaposes zero-shot and few-shot scenarios to delineate the impact of CoT disruption across different prompting contexts. It assesses the significance of stepwise logical progression in the model's reasoning efficacy and its ability to reach correct conclusions despite disruptions in the sequence of sentences .

  6. Comparative Analysis: The paper provides a comparative analysis of performance across various task types within the ShortcutQA dataset, demonstrating the effectiveness of shortcut reasoning in mathematical, analytical, and verbal reasoning tasks compared to established baselines. "Innovative Shortcut" and "Quick Conclude" prompts show significant improvements over traditional methods .

Overall, the paper's innovative characteristics, such as the "break the chain" strategy, efficiency gains, ShortcutQA dataset, model size impact, experimental framework, and comparative analysis, offer valuable insights into advancing the reasoning capabilities of LLMs and enhancing their performance in various tasks .


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 papers exist in the field of large language models and reasoning strategies. Noteworthy researchers in this area include Mengru Ding, Hanmeng Liu, Zhizhang Fu, Jian Song, Wenbo Xie, and Yue Zhang . The key solution proposed in the paper involves integrating human-like heuristics and shortcuts into language models through "break the chain" strategies. These strategies disrupt traditional Chain-of-Thought (CoT) processes by introducing controlled variables to assess their effectiveness, enabling language models to quickly exploit reasoning clues and bypass detailed procedural steps .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the resilience and limitations of Large Language Models (LLMs) in employing Chain-of-Thought (CoT) reasoning through a novel experimental framework called "break the chain" . This approach aimed to elucidate the conditions under which CoT reasoning may falter, providing insights into the underlying mechanisms of LLMs' reasoning capabilities . The methodology involved perturbing the sequence of sentences within in-context examples to misalign the logical progression typically demonstrated in CoT reasoning, testing the model's ability to maintain coherent and accurate reasoning despite the disordered presentation of steps . Additionally, the experiments included few-shot scenarios to assess the impact of CoT disruption across different prompting contexts and zero-shot experiments to evaluate the efficacy of zero-shot CoT prompts . The study rigorously tested LLMs' ability to generalize across different difficulty levels and domains, focusing on tasks such as arithmetic reasoning, commonsense deduction, and logical reasoning .


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

The dataset used for quantitative evaluation in the study is the ShortcutQA dataset . The code for the study is open source and can be accessed at https://anonymous.com .


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 substantial support for the scientific hypotheses that need to be verified. The research introduces a novel experimental framework aimed at disrupting Chain-of-Thought (CoT) reasoning, known as the "break the chain" approach, to investigate the conditions under which CoT reasoning may falter . By perturbing the logical progression in few-shot scenarios and crafting zero-shot prompts that obviate the need for reasoning chains, the study aims to evaluate the resilience of Large Language Models (LLMs) in reasoning processes . The findings challenge the prevailing assumption that CoT invariably enhances LLM performance, indicating that specific prompts, even without detailed reasoning, can yield comparable or superior outcomes . Moreover, the study highlights the nuanced interplay between prompt nature and LLM performance, emphasizing the importance of contextual alignment in reasoning sequences .

Furthermore, the research delves into the impact of model size on CoT's relative performance advantage over other prompts across various tasks, showing that smaller models exhibit a more pronounced reliance on CoT, especially as the model size decreases . The study also presents a comparative analysis of performance across different task types within the ShortcutQA dataset, demonstrating that "break the chain" prompts, particularly the "Innovative Shortcut" and "Quick Conclude" prompts, outperform established baselines in mathematical, analytical, and verbal reasoning tasks . These results provide empirical evidence supporting the effectiveness of heuristic shortcuts in enhancing efficiency without compromising performance across various models .

In conclusion, the experiments and results outlined in the paper offer valuable insights into the resilience and limitations of LLMs in employing CoT reasoning, shedding light on the efficacy of disruptive approaches like "break the chain" prompts and the potential benefits of heuristic shortcuts in improving the problem-solving capabilities of AI systems . The study contributes to the discourse on reasoning processes in LLMs and underscores the importance of exploring alternative reasoning strategies beyond traditional CoT approaches .


What are the contributions of this paper?

The paper "Break the Chain: Large Language Models Can be Shortcut Reasoners" makes several key contributions:

  • It critically evaluates Chain-of-Thought (CoT) prompting, extending beyond arithmetic to include complex logical and commonsense reasoning tasks, areas where standard CoT methods fall short .
  • The paper proposes integrating human-like heuristics and shortcuts into language models (LMs) through "break the chain" strategies, disrupting traditional CoT processes to assess their efficacy .
  • Innovative zero-shot prompting strategies are developed to encourage the use of shortcuts, enabling LMs to quickly exploit reasoning clues and bypass detailed procedural steps .
  • The experiments conducted across various LMs, both commercial and open-source, reveal that LMs maintain effective performance with "break the chain" strategies .
  • The introduction of ShortcutQA, a dataset specifically designed to evaluate reasoning through shortcuts, serves as a robust challenge to LMs and a benchmark for enhancing reasoning efficiency in AI .

What work can be continued in depth?

Further research in the field of Large Language Models (LLMs) and Chain-of-Thought (CoT) prompting can be expanded in several areas based on the existing studies:

  • Exploration of Shortcut Reasoning: Future studies can delve deeper into the effectiveness and implications of shortcut reasoning in LLMs. The use of heuristic shortcuts, akin to human intuitive problem-solving, can significantly enhance computational efficiency and model performance .
  • Investigation of CoT Mechanisms: Research efforts can focus on demystifying the mechanisms by which LLMs generate CoT responses. Understanding the underlying processes of CoT reasoning can lead to improved model performance on complex tasks .
  • Enhancing CoT Faithfulness: There is a need to address the issue of faithfulness in CoT reasoning. Studies can aim to improve the reliability of CoT explanations by ensuring that the articulated reasoning aligns with the model's underlying logic .
  • Evaluation of CoT Consistency: Further research can explore the impact of disrupting the order of reasoning chains on CoT consistency. By examining the effects of disturbed reasoning chains, researchers can gain insights into maintaining CoT coherence .
  • Comparative Analysis of Model Sizes: Future studies can focus on analyzing the performance trends of different model sizes concerning CoT prompts. Understanding how model size influences the efficacy of CoT strategies can provide valuable insights for model development and optimization .
  • Generalizability of Experimental Conclusions: It is essential to assess the generalizability of experimental findings across various model configurations. Conducting experiments on different models can help validate the conclusions and ensure the robustness of the research outcomes .
  • Evaluation of Token Limits: Investigating the impact of token limits on model performance can be a valuable area of research. Understanding how token constraints affect reasoning capabilities can lead to optimizations in model design and training .

Tables

4

Introduction
Background
Emergence of Chain-of-Thought (CoT) prompting in LLMs
Purpose of CoT in complex reasoning tasks
Objective
To evaluate CoT limitations and propose "break the chain" strategies
Investigate the impact of model size and dataset on reasoning performance
Method
Data Collection
Usage of ShortcutQA dataset
Comparison of models in few-shot and zero-shot scenarios
Dataset: ShortcutQA
Description and creation
Role in testing reasoning through shortcuts
Data Analysis
CoT Performance
Evaluation of CoT effectiveness in different model sizes
Observations on few-shot and zero-shot scenarios
"Break the Chain" Strategies
Implementation and results of proposed strategies
Comparison with CoT in terms of efficiency and performance
Prompt Design and Task Nature
Analysis of the influence of prompt design on reasoning outcomes
Exploration of task complexity and CoT/shortcut trade-offs
Results and Discussion
Performance trends across model sizes
The role of heuristics in improving computational efficiency
Implications for AI problem-solving and reasoning enhancement
Conclusion
Summary of findings and contributions to the field
Future directions for research on CoT and shortcut-based reasoning in LLMs
Limitations and Future Work
Current study's constraints and potential improvements
Open questions and areas for further investigation
Basic info
papers
computation and language
artificial intelligence
Advanced features
Insights
How do model size and "break the chain" strategies affect reasoning in different scenarios?
What strategies do researchers propose to improve efficiency in Chain-of-Thought prompting?
How does the ShortcutQA dataset contribute to the analysis?
What does the paper focus on in terms of large language models?

Break the Chain: Large Language Models Can be Shortcut Reasoners

Mengru Ding, Hanmeng Liu, Zhizhang Fu, Jian Song, Wenbo Xie, Yue Zhang·June 04, 2024

Summary

This paper critically examines Chain-of-Thought (CoT) prompting in large language models, particularly its limitations in complex reasoning tasks. Researchers propose "break the chain" strategies that incorporate human-like heuristics to improve efficiency without compromising performance. The ShortcutQA dataset is introduced to test reasoning through shortcuts, revealing that smaller models can benefit from CoT in few-shot scenarios, while zero-shot models with shortcut reasoning often outperform or match CoT. As model size increases, "break the chain" strategies become more effective, with shortcut reasoning enhancing computational efficiency. The study also highlights the importance of understanding the relationship between prompt design and task nature, as well as the trade-offs between CoT and shortcut-based reasoning. The research contributes to the ongoing debate on the best approaches for enhancing AI problem-solving and reasoning capabilities.
Mind map
Comparison with CoT in terms of efficiency and performance
Implementation and results of proposed strategies
Observations on few-shot and zero-shot scenarios
Evaluation of CoT effectiveness in different model sizes
Role in testing reasoning through shortcuts
Description and creation
Exploration of task complexity and CoT/shortcut trade-offs
Analysis of the influence of prompt design on reasoning outcomes
"Break the Chain" Strategies
CoT Performance
Dataset: ShortcutQA
Investigate the impact of model size and dataset on reasoning performance
To evaluate CoT limitations and propose "break the chain" strategies
Purpose of CoT in complex reasoning tasks
Emergence of Chain-of-Thought (CoT) prompting in LLMs
Open questions and areas for further investigation
Current study's constraints and potential improvements
Future directions for research on CoT and shortcut-based reasoning in LLMs
Summary of findings and contributions to the field
Implications for AI problem-solving and reasoning enhancement
The role of heuristics in improving computational efficiency
Performance trends across model sizes
Prompt Design and Task Nature
Data Analysis
Data Collection
Objective
Background
Limitations and Future Work
Conclusion
Results and Discussion
Method
Introduction
Outline
Introduction
Background
Emergence of Chain-of-Thought (CoT) prompting in LLMs
Purpose of CoT in complex reasoning tasks
Objective
To evaluate CoT limitations and propose "break the chain" strategies
Investigate the impact of model size and dataset on reasoning performance
Method
Data Collection
Usage of ShortcutQA dataset
Comparison of models in few-shot and zero-shot scenarios
Dataset: ShortcutQA
Description and creation
Role in testing reasoning through shortcuts
Data Analysis
CoT Performance
Evaluation of CoT effectiveness in different model sizes
Observations on few-shot and zero-shot scenarios
"Break the Chain" Strategies
Implementation and results of proposed strategies
Comparison with CoT in terms of efficiency and performance
Prompt Design and Task Nature
Analysis of the influence of prompt design on reasoning outcomes
Exploration of task complexity and CoT/shortcut trade-offs
Results and Discussion
Performance trends across model sizes
The role of heuristics in improving computational efficiency
Implications for AI problem-solving and reasoning enhancement
Conclusion
Summary of findings and contributions to the field
Future directions for research on CoT and shortcut-based reasoning in LLMs
Limitations and Future Work
Current study's constraints and potential improvements
Open questions and areas for further investigation
Key findings
8

Paper digest

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

The paper aims to investigate the resilience and limitations of Large Language Models (LLMs) in employing Chain-of-Thought (CoT) reasoning by introducing a novel experimental framework called "break the chain." This approach seeks to understand the conditions under which CoT reasoning may fail and provides insights into the reasoning capabilities of LLMs . The study explores the effectiveness, limitations, and mechanisms of CoT by comparing it with different prompts derived from the "break the chain" strategy in both few-shot and zero-shot scenarios, as well as pioneering the use of shortcut reasoning prompts to enhance problem-solving capabilities . The research introduces the ShortcutQA dataset to evaluate the ability of LLMs to employ heuristic shortcuts, aiming to minimize computational demands while maintaining or enhancing performance accuracy . The paper addresses the challenge of optimizing reasoning processes in LLMs by exploring the impact of disrupted CoT demonstrations, the effectiveness of shortcut reasoning prompts, and the nuanced interplay between prompt nature and LLM performance . This problem is not entirely new but represents a novel approach to evaluating and enhancing the reasoning capabilities of LLMs through the introduction of shortcut reasoning strategies and the examination of CoT limitations .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis that Large Language Models (LLMs) can effectively employ heuristic shortcuts, similar to human cognitive shortcuts, to efficiently solve complex problems . The study investigates the resilience and limitations of LLMs in employing Chain-of-Thought (CoT) reasoning by introducing experimental frameworks that disrupt the logical progression of thought to understand the conditions under which CoT reasoning may falter . Additionally, the research explores the impact of CoT disruption in both zero-shot and few-shot scenarios to evaluate the model's ability to maintain coherent and accurate reasoning despite the disordered presentation of steps .


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

The paper "Break the Chain: Large Language Models Can be Shortcut Reasoners" introduces innovative ideas, methods, and models to evaluate the reasoning capabilities of Large Language Models (LLMs) . One key concept is the "break the chain" strategy, which challenges the traditional Chain-of-Thought (CoT) reasoning by disrupting the logical progression of thought sequences . This approach aims to uncover the conditions under which CoT reasoning may fail, providing insights into the underlying mechanisms of LLMs' reasoning abilities .

The study explores the impact of token limits on model performance by experimenting with different contexts, such as zero-shot and few-shot scenarios, to understand the implications of CoT disruption . The research delves into the significance of stepwise logical progression in model reasoning efficacy and evaluates the model's ability to maintain coherent and accurate reasoning despite disturbances in the sequence of sentences .

Additionally, the paper introduces the ShortcutQA dataset, which focuses on questions requiring shortcut reasoning, a form of intuitive problem-solving that deviates from traditional step-by-step logical deduction . This dataset aims to test the hypothesis that LLMs can effectively utilize heuristic shortcuts similar to human cognitive shortcuts to efficiently solve complex problems .

Furthermore, the study documents experiments with models of varying sizes under CoT and "break the chain" conditions, highlighting that smaller models exhibit a more pronounced reliance on CoT, especially as the model size decreases . The findings challenge the assumption that CoT invariably enhances LLM performance, suggesting that specific prompts, even without detailed reasoning, can yield comparable or superior outcomes .

Overall, the paper presents a comprehensive evaluation of CoT reasoning in LLMs, emphasizing the limitations and potential enhancements through innovative strategies like the "break the chain" approach and the use of the ShortcutQA dataset . These new ideas and methods provide valuable insights into improving the problem-solving capabilities of AI systems in real-world applications . The paper "Break the Chain: Large Language Models Can be Shortcut Reasoners" introduces innovative characteristics and advantages compared to previous methods in evaluating Large Language Models (LLMs) .

  1. "Break the Chain" Strategy: The paper proposes the "break the chain" strategy, which challenges the traditional Chain-of-Thought (CoT) reasoning by disrupting the logical progression of thought sequences . This approach aims to uncover the conditions under which CoT reasoning may fail, providing insights into the underlying mechanisms of LLMs' reasoning abilities .

  2. Efficiency and Performance: Shortcut reasoning significantly reduces token consumption, offering a vital advantage in computational efficiency. It not only conserves resources but also consistently outperforms traditional CoT methods across various datasets, highlighting its robustness and scalability in enhancing LLM performance .

  3. ShortcutQA Dataset: The introduction of the ShortcutQA dataset focuses on questions requiring shortcut reasoning, deviating from traditional step-by-step logical deduction. This dataset challenges current LLMs and sets a benchmark for future enhancements, providing a robust platform for testing and refining next-generation models .

  4. Model Size Impact: The study documents experiments with models of varying sizes under CoT and "break the chain" conditions, revealing that smaller models exhibit a more pronounced reliance on CoT, especially as the model size decreases. This challenges the assumption that CoT invariably enhances LLM performance, suggesting that specific prompts, even without detailed reasoning, can yield comparable or superior outcomes .

  5. Experimental Framework: The research methodology juxtaposes zero-shot and few-shot scenarios to delineate the impact of CoT disruption across different prompting contexts. It assesses the significance of stepwise logical progression in the model's reasoning efficacy and its ability to reach correct conclusions despite disruptions in the sequence of sentences .

  6. Comparative Analysis: The paper provides a comparative analysis of performance across various task types within the ShortcutQA dataset, demonstrating the effectiveness of shortcut reasoning in mathematical, analytical, and verbal reasoning tasks compared to established baselines. "Innovative Shortcut" and "Quick Conclude" prompts show significant improvements over traditional methods .

Overall, the paper's innovative characteristics, such as the "break the chain" strategy, efficiency gains, ShortcutQA dataset, model size impact, experimental framework, and comparative analysis, offer valuable insights into advancing the reasoning capabilities of LLMs and enhancing their performance in various tasks .


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 papers exist in the field of large language models and reasoning strategies. Noteworthy researchers in this area include Mengru Ding, Hanmeng Liu, Zhizhang Fu, Jian Song, Wenbo Xie, and Yue Zhang . The key solution proposed in the paper involves integrating human-like heuristics and shortcuts into language models through "break the chain" strategies. These strategies disrupt traditional Chain-of-Thought (CoT) processes by introducing controlled variables to assess their effectiveness, enabling language models to quickly exploit reasoning clues and bypass detailed procedural steps .


How were the experiments in the paper designed?

The experiments in the paper were designed to evaluate the resilience and limitations of Large Language Models (LLMs) in employing Chain-of-Thought (CoT) reasoning through a novel experimental framework called "break the chain" . This approach aimed to elucidate the conditions under which CoT reasoning may falter, providing insights into the underlying mechanisms of LLMs' reasoning capabilities . The methodology involved perturbing the sequence of sentences within in-context examples to misalign the logical progression typically demonstrated in CoT reasoning, testing the model's ability to maintain coherent and accurate reasoning despite the disordered presentation of steps . Additionally, the experiments included few-shot scenarios to assess the impact of CoT disruption across different prompting contexts and zero-shot experiments to evaluate the efficacy of zero-shot CoT prompts . The study rigorously tested LLMs' ability to generalize across different difficulty levels and domains, focusing on tasks such as arithmetic reasoning, commonsense deduction, and logical reasoning .


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

The dataset used for quantitative evaluation in the study is the ShortcutQA dataset . The code for the study is open source and can be accessed at https://anonymous.com .


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 substantial support for the scientific hypotheses that need to be verified. The research introduces a novel experimental framework aimed at disrupting Chain-of-Thought (CoT) reasoning, known as the "break the chain" approach, to investigate the conditions under which CoT reasoning may falter . By perturbing the logical progression in few-shot scenarios and crafting zero-shot prompts that obviate the need for reasoning chains, the study aims to evaluate the resilience of Large Language Models (LLMs) in reasoning processes . The findings challenge the prevailing assumption that CoT invariably enhances LLM performance, indicating that specific prompts, even without detailed reasoning, can yield comparable or superior outcomes . Moreover, the study highlights the nuanced interplay between prompt nature and LLM performance, emphasizing the importance of contextual alignment in reasoning sequences .

Furthermore, the research delves into the impact of model size on CoT's relative performance advantage over other prompts across various tasks, showing that smaller models exhibit a more pronounced reliance on CoT, especially as the model size decreases . The study also presents a comparative analysis of performance across different task types within the ShortcutQA dataset, demonstrating that "break the chain" prompts, particularly the "Innovative Shortcut" and "Quick Conclude" prompts, outperform established baselines in mathematical, analytical, and verbal reasoning tasks . These results provide empirical evidence supporting the effectiveness of heuristic shortcuts in enhancing efficiency without compromising performance across various models .

In conclusion, the experiments and results outlined in the paper offer valuable insights into the resilience and limitations of LLMs in employing CoT reasoning, shedding light on the efficacy of disruptive approaches like "break the chain" prompts and the potential benefits of heuristic shortcuts in improving the problem-solving capabilities of AI systems . The study contributes to the discourse on reasoning processes in LLMs and underscores the importance of exploring alternative reasoning strategies beyond traditional CoT approaches .


What are the contributions of this paper?

The paper "Break the Chain: Large Language Models Can be Shortcut Reasoners" makes several key contributions:

  • It critically evaluates Chain-of-Thought (CoT) prompting, extending beyond arithmetic to include complex logical and commonsense reasoning tasks, areas where standard CoT methods fall short .
  • The paper proposes integrating human-like heuristics and shortcuts into language models (LMs) through "break the chain" strategies, disrupting traditional CoT processes to assess their efficacy .
  • Innovative zero-shot prompting strategies are developed to encourage the use of shortcuts, enabling LMs to quickly exploit reasoning clues and bypass detailed procedural steps .
  • The experiments conducted across various LMs, both commercial and open-source, reveal that LMs maintain effective performance with "break the chain" strategies .
  • The introduction of ShortcutQA, a dataset specifically designed to evaluate reasoning through shortcuts, serves as a robust challenge to LMs and a benchmark for enhancing reasoning efficiency in AI .

What work can be continued in depth?

Further research in the field of Large Language Models (LLMs) and Chain-of-Thought (CoT) prompting can be expanded in several areas based on the existing studies:

  • Exploration of Shortcut Reasoning: Future studies can delve deeper into the effectiveness and implications of shortcut reasoning in LLMs. The use of heuristic shortcuts, akin to human intuitive problem-solving, can significantly enhance computational efficiency and model performance .
  • Investigation of CoT Mechanisms: Research efforts can focus on demystifying the mechanisms by which LLMs generate CoT responses. Understanding the underlying processes of CoT reasoning can lead to improved model performance on complex tasks .
  • Enhancing CoT Faithfulness: There is a need to address the issue of faithfulness in CoT reasoning. Studies can aim to improve the reliability of CoT explanations by ensuring that the articulated reasoning aligns with the model's underlying logic .
  • Evaluation of CoT Consistency: Further research can explore the impact of disrupting the order of reasoning chains on CoT consistency. By examining the effects of disturbed reasoning chains, researchers can gain insights into maintaining CoT coherence .
  • Comparative Analysis of Model Sizes: Future studies can focus on analyzing the performance trends of different model sizes concerning CoT prompts. Understanding how model size influences the efficacy of CoT strategies can provide valuable insights for model development and optimization .
  • Generalizability of Experimental Conclusions: It is essential to assess the generalizability of experimental findings across various model configurations. Conducting experiments on different models can help validate the conclusions and ensure the robustness of the research outcomes .
  • Evaluation of Token Limits: Investigating the impact of token limits on model performance can be a valuable area of research. Understanding how token constraints affect reasoning capabilities can lead to optimizations in model design and training .
Tables
4
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