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Beyond Accuracy: Evaluating the Reasoning Behavior of Large Language Models -- A Survey

Philipp Mondorf, Barbara Plank

arXiv:2404.01869v2cs.CLcs.AI

TL;DR

The depth of LLM reasoning remains uncertain, in part because research often emphasizes task performance over reasoning behavior. This survey reviews behavioral evidence and evaluation methods, finding that models can handle training-aligned problems but face conceptual challenges out of distribution.

  • Problem

    The depth of LLMs’ reasoning abilities remains uncertain, while research and surveys have largely emphasized downstream-task performance rather than reasoning behavior.

    Source: §1 · pp. 1–2

  • Method

    The survey reviews studies of LLM reasoning behavior beyond task accuracy and organizes prevalent evaluation methods into a taxonomy.

    Source: §1 · p. 2

  • Results

    LLMs show proficiency on reasoning problems aligned with training data but often encounter significant conceptual challenges in out-of-distribution scenarios.

    Source: §6 · p. 10

  • Takeaways & Limitations

    The authors call for more nuanced analyses, especially in novel scenarios, and further research comparing human and LLM reasoning.

    Source: §5 · p. 10

    The survey covers core logical, mathematical, and causal reasoning tasks, leaving integrated-task behavior for future work.

    Source: §2.1 · p. 3

Abstract

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Large language models (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.

1 Introduction

The introduction frames whether LLMs’ reasoning successes reflect human-like reasoning or training-data memorization. It presents a survey of reasoning behavior beyond task accuracy and evaluation methods for analyzing it.

1 Introduction

  • LLMs’ strong performance on reasoning tasks has prompted debate over whether they reason like humans or mainly reflect training-data memorization.The introduction identifies model complexity and closed-source training data as challenges for resolving this question.Source: §1 · p. 1
  • Its research questions ask how LLMs behave across diverse reasoning tasks and which evaluation methods are used to assess that behavior.The introduction situates reasoning as important to problem-solving and decision-making.Source: §1 · pp. 1–2
  • The survey reviews research that examines LLM reasoning behavior beyond task accuracy, addressing a gap in surveys centered on downstream-task performance.The authors also propose a taxonomy of methods for analyzing reasoning dynamics.Source: §1 · p. 2

2 Terminology

The section defines reasoning and reasoning behavior as process-oriented concepts, distinct from outcome-focused performance. It distinguishes core from integrated tasks and limits the survey’s task coverage to core reasoning.

2 Terminology

  • Reasoning is drawing conclusions from available information, usually a set of premises.The survey uses a broad definition and points readers to other surveys for domain-specific definitions.Source: §2 · p. 2
  • Reasoning behavior concerns how a system responds during a task, while reasoning performance concerns outcomes such as accuracy or efficiency.Analyzing behavior can provide insight into the reasoning process beyond performance measures.Source: §2 · p. 2

2.1 A Categorization of Reasoning Tasks

  • Core tasks assess an isolated reasoning skill, whereas integrated tasks require concurrent use of multiple reasoning skills.The survey gives logical, mathematical, and causal tasks as core examples, and commonsense or scientific tasks as integrated examples.Source: §2.1 · p. 3, Figure 1 · p. 3
  • The survey covers LLM behavior on core logical, mathematical, and causal reasoning tasks, leaving integrated-task behavior for future work.Its scope is therefore limited to those three core task types.Source: §2.1 · p. 3

3 Reasoning Behavior of LLMs

The section reviews LLM behavior across logical, mathematical, and causal reasoning tasks, finding that apparent competence often relies on familiar patterns and weakens under distribution shifts or task changes.

3.1 Behavior in Logical Reasoning Tasks

  • Logical reasoning is divided into deductive conclusions that necessarily follow from premises and defeasible inductive or abductive conclusions that are only probable.Induction generalizes from specific instances, whereas abduction forms plausible hypotheses to explain observed data.Source: §3.1 · p. 4

3.1.1 Behavior in Deductive Reasoning Tasks

  • Deductive studies report shortcut matching, sensitivity to premise order and statistical cues, weak logical-operator handling, human-like biases, and evidence of layered reasoning mechanisms.Saparov & He (2023) find larger models generate more valid and atomic steps, but low utility can produce misleading steps; Dutta et al. (2024) report concurrent pathways, unlike the step-wise processing described by Hou et al. (2023).Source: §3.1.1 · pp. 4–5

3.1.2 Behavior in Inductive Reasoning Tasks

  • Inductive models can infer rules but often struggle to apply them consistently, ensure they fit premises and tasks, or handle premise non-monotonicity.The review also reports sensitivity to task-description changes and rules that diverge from human-produced generalizations.Source: §3.1.2 · p. 5

3.1.3 Behavior in Abductive Reasoning Tasks

  • Abductive studies report limited coherent explanation beyond familiar patterns, difficulty weakening or strengthening hypotheses, ungrounded rationales, and multi-hop reasoning problems.Fine-tuned models can still contradict themselves by producing identical statements to strengthen and weaken the same hypothesis.Source: §3.1.3 · p. 6

3.2 Behavior in Mathematical Reasoning Tasks

  • Mathematical reasoning studies find memorization-linked inconsistency, distraction by irrelevant or altered problem details, intuitive errors, and goal misgeneralization.Layer-wise analysis reports stronger mathematical problem-solving in higher LLaMA layers, while lower layers appear to lack basic arithmetic and factual knowledge.Source: §3.2 · p. 6

3.3 Behavior in Causal Reasoning Tasks

  • Causal studies report stronger performance on familiar associational queries than interventional or counterfactual tasks, with continued difficulty generalizing out of distribution.Models can identify a causal structure when hypotheses are provided but struggle when they must infer the hypotheses from limited data.Source: §3.3 · p. 7

3 Reasoning Behavior of LLMs

  • Across reasoning tasks, LLMs often falter out of distribution, with slight context changes impairing performance and multi-step reasoning reduced to shortcuts.The review also reports conceptual errors in logic, mathematics, and causal reasoning, particularly in counterfactual setups.Source: §3.3 · p. 7
  • Figure 2 presents a taxonomy of evaluation methods and lists representative approaches for each method.Source: Figure 2 · p. 8

4 Evaluation Methods

The section groups reasoning-behavior evaluations into conclusion-based, rationale-based, interactive, and mechanistic approaches; no standardized methodology currently exists for assessing LLM reasoning capabilities.

4.1 Conclusion-Based Evaluation

  • Conclusion-based evaluations analyze final answers for errors, biases, context sensitivity, or output predispositions, but can miss rationales and be undermined by leakage or answer–rationale mismatch.Dynamic functional benchmarks can probe reasoning on quarterly dataset snapshots.Source: §4.1 · pp. 8–9

4.2 Rationale-Based Evaluation

  • Rationale-based evaluations inspect traces for validity and coherence, using formal representations or metrics for structured traces and human or agent review for less structured ones.These methods offer more nuanced analysis but are harder to automate and scale.Source: §4.2 · p. 9

4.3 Interactive Evaluation

  • Interactive evaluations adapt tasks or challenge conclusions through dialogue and games, offering tailored insight but often costly assessment and weaker standardization and reproducibility.Adaptive evaluations select tasks based on model responses.Source: §4.3 · p. 9

4.4 Mechanistic Evaluation

  • Mechanistic evaluations examine attention, activation flows, and layer functions to probe how responses arise, but remain compute-intensive and may not generalize across models or tasks.The approach focuses on intrinsic processes, in contrast with outcome- or rationale-focused evaluations.Source: §4.4 · p. 9

5 Discussion

The discussion characterizes current LLM reasoning as strongly tied to training data and limited in out-of-distribution settings, while calling for further study of human–LLM differences.

5 Discussion

  • Current LLMs more closely resemble stochastic parrots than systematic reasoners, despite strong performance on prominent reasoning tasks.The review reports proficiency on problems aligned with training data alongside conceptual errors and limitations out of distribution.Source: §5 · p. 9
  • Apparent reasoning success may largely reflect memorization, while benchmark leakage raises concerns about conclusions drawn from static benchmark performance.The authors advocate nuanced analyses in novel scenarios the models have not previously encountered.Source: §5 · p. 10
  • Human robust reasoning and generalization from limited data remain unmatched by current LLMs, motivating further research to identify what supports systematic reasoning.The discussion points to restrictive autoregressive pre-training as a fundamental difference between human and LLM reasoning.Source: §5 · p. 10

6 Conclusion

The survey reviews LLM reasoning behavior beyond accuracy and evaluation frameworks, finding difficulties out of distribution and cautioning against shallow accuracy alone.

6 Conclusion

  • The survey examines LLM behavior in logical, mathematical, and causal reasoning and outlines evaluation frameworks with their strengths and limitations.Its focus is literature evaluating reasoning beyond task accuracy.Source: §6 · p. 10
  • LLMs show proficiency on reasoning problems aligned with their training data but often encounter significant conceptual challenges out of distribution.Source: §6 · p. 10
  • Given these findings and benchmark leakage, the authors caution against relying solely on shallow accuracy for static reasoning tasks and advocate more sophisticated assessments.Source: §6 · p. 10

A Human-Like Reasoning Behavior in LLMs

Studies reviewed here find both human-like reasoning biases and errors in LLMs, alongside difficulties with logical inference and differences from human responses.

A Human-Like Reasoning Behavior in LLMs

B Further Details on Evaluation Methods

The section organizes methods for evaluating LLM reasoning behavior into conclusion-based, rationale-based, interactive, and mechanistic approaches.

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