Position: On the Methodological Pitfalls of Evaluating Base LLMs for Reasoning

2025-11-13

AI Summary

Current evaluation of reasoning capabilities in foundation large language models (LLMs)—trained solely via unsupervised pretraining—suffers from a fundamental methodological flaw: the pretraining objective (statistical language modeling) is inherently misaligned with normative reasoning criteria (e.g., logical validity), rendering apparent “reasonableness” a statistical byproduct rather than genuine reasoning. This misalignment undermines two implicit assumptions: (a) model outputs reflect an active pursuit of correct answers, and (b) reasoning behaviors of foundation models generalize to instruction-tuned LLMs. Method: We conduct a conceptual analysis contrasting pretraining mechanisms with reasoning evaluation paradigms. Contribution/Results: We systematically critique prevailing evaluation logic and advocate for a reconceptualized reasoning assessment framework grounded in task-objective alignment. This shift promotes more rigorous, interpretable, and theoretically sound LLM reasoning research.

Technology Category

• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Natural Language Processing: (Large) Language Models
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Existing work investigates the reasoning capabilities of large language models (LLMs) to uncover their limitations, human-like biases and underlying processes. Such studies include evaluations of base LLMs (pre-trained on unlabeled corpora only) for this purpose. Our position paper argues that evaluating base LLMs'reasoning capabilities raises inherent methodological concerns that are overlooked in such existing studies. We highlight the fundamental mismatch between base LLMs'pretraining objective and normative qualities, such as correctness, by which reasoning is assessed. In particular, we show how base LLMs generate logically valid or invalid conclusions as coincidental byproducts of conforming to purely linguistic patterns of statistical plausibility. This fundamental mismatch challenges the assumptions that (a) base LLMs'outputs can be assessed as their bona fide attempts at correct answers or conclusions; and (b) conclusions about base LLMs'reasoning can generalize to post-trained LLMs optimized for successful instruction-following. We call for a critical re-examination of existing work that relies implicitly on these assumptions, and for future work to account for these methodological pitfalls.

Problem

• Evaluating base LLMs' reasoning raises methodological concerns overlooked in studies
• Base LLMs' pretraining mismatches normative reasoning qualities like correctness
• Assumptions about base LLMs' reasoning generalizing to post-trained models are challenged

Innovation

• Evaluating base LLMs challenges methodological assumptions
• Base LLMs produce conclusions as statistical byproducts
• Pretraining mismatch questions reasoning assessment validity