Beyond Accuracy: Evaluating the Reasoning Behavior of Large Language Models -- A Survey (2404.01869v2)

Abstract: LLMs have recently shown impressive performance on tasks involving reasoning, leading to a lively debate on whether these models possess reasoning capabilities similar to humans. However, despite these successes, the depth of LLMs' reasoning abilities remains uncertain. This uncertainty partly stems from the predominant focus on task performance, measured through shallow accuracy metrics, rather than a thorough investigation of the models' reasoning behavior. This paper seeks to address this gap by providing a comprehensive review of studies that go beyond task accuracy, offering deeper insights into the models' reasoning processes. Furthermore, we survey prevalent methodologies to evaluate the reasoning behavior of LLMs, emphasizing current trends and efforts towards more nuanced reasoning analyses. Our review suggests that LLMs tend to rely on surface-level patterns and correlations in their training data, rather than on sophisticated reasoning abilities. Additionally, we identify the need for further research that delineates the key differences between human and LLM-based reasoning. Through this survey, we aim to shed light on the complex reasoning processes within LLMs.

• The paper introduces a taxonomy differentiating core and integrated reasoning tasks to assess LLMs’ capabilities beyond surface-level accuracy.
• It demonstrates that LLMs often rely on memorization and statistical patterns rather than systematic reasoning, particularly in logical and mathematical tasks.
• The survey advocates novel evaluation frameworks, including rationale-based and interactive methods, to more accurately capture genuine reasoning behaviors in LLMs.

Evaluating the Reasoning Behavior of LLMs

The paper "Beyond Accuracy: Evaluating the Reasoning Behavior of LLMs -- A Survey" examines the reasoning capabilities of LLMs by moving beyond accuracy metrics to provide deeper insights into their reasoning behaviors. It highlights the limitations of current evaluations focused solely on task accuracy and suggests that LLMs rely heavily on surface-level patterns from training data rather than demonstrating true reasoning abilities. The paper introduces a categorization of reasoning tasks and explores evaluation methods that offer a more nuanced understanding of reasoning dynamics in LLMs.

Introduction to Reasoning in LLMs

The perennial question surrounding LLMs is whether their capabilities include real reasoning or whether they simply mimic reasoning due to large-scale data exposure. Past debates often categorize these models as "castles in the air" due to their reliance on vast parameters and training data without foundational reasoning skills. The paper aims to clarify these concerns by:

• Providing insights into models' behaviors in diverse reasoning tasks.
• Offering a taxonomy of methods for assessing reasoning beyond conventional accuracy.

The survey suggests the need for research delineating differences between human reasoning and LLM-based reasoning.

Figure 1: Schematic overview of the two types of reasoning tasks distinguished in this survey.

Reasoning Task Categorization

Two primary task types are identified:

• Core Reasoning Tasks: Focus on assessing singular reasoning abilities, such as logical, mathematical, or causal tasks.
• Integrated Reasoning Tasks: These require multiple reasoning skills concurrently, such as commonsense or scientific reasoning tasks.

The paper emphasizes evaluating core tasks, particularly logical, mathematical, and causal reasoning, to understand LLMs' reasoning behaviors in isolated settings.

Key Findings on Reasoning Behavior

Logical Reasoning

Differences in reasoning capabilities between model sizes are apparent. Larger models tend to perform better at generating valid and atomic steps but often produce steps with low utility. Furthermore, models frequently rely on statistical features rather than systematic reasoning. This impacts tasks involving logical operators like negations, which are understood poorly by many models.

Mathematical Reasoning

LLMs tend to memorize rather than reason systematically, as demonstrated by varied performance on mathematical problems formulated differently but based on the same reasoning process. The influence of term frequency from pre-training data highlights a model's struggle to generalize.

Causal Reasoning

Current models showcase limited intrinsic abilities to grasp or construct causal relationships outside of known factual patterns. Causal reasoning, particularly counterfactual reasoning tasks, reveals a specific area where models exhibit significant shortcomings.

Evaluation Methods for Reasoning

The taxonomy of evaluation methods includes:

• Conclusion-Based Evaluation: Focuses on the model's final answer rather than the reasoning trace, although it can uncover conceptual errors and the model's confidence or preference for certain answers.
• Rationale-Based Evaluation: Examines the reasoning trace or rationale generated by models, using structured parsing or qualitative inspections.
• Interactive Evaluation: Engages the model dynamically during evaluation, including adaptive testing or dialectic evaluation methods.
• Mechanistic Evaluation: Investigates the model's internal processes, using methods like layer probing or activation patching to understand the reasoning mechanics.

The paper suggests these methods can provide valuable insights that go beyond simple accuracy metrics.

Discussion and Implications

The research underscores differences between the apparent proficiency of LLMs in reasoning tasks and their genuine capability to reason. It highlights various challenges, particularly when tasked with scenarios trying the limits of their training data. The paper encourages developing more sophisticated reasoning evaluation methods that mirror human reasoning evaluation processes.

Conclusion

The survey concludes that while LLMs demonstrate some reasoning abilities, current evaluations fail to capture the depth of reasoning skills as observed in human cognition. There’s a need for innovative evaluation frameworks that focus on nuanced aspects of reasoning, providing a more comprehensive picture of LLMs' reasoning behaviors. This approach could guide future advancements in AI research, particularly in reasoning capabilities of LLMs.