论文精选

LLM推理质量评估新框架:多维度行为框架揭示准确率之外的盲区

Measuring Reasoning Quality in LLMs: A Multi-Dimensional Behavioral Framework

精选理由

这个框架解决了「只看答案正确率」的评估盲区,做模型选型或合规审计的团队会发现,原来高分模型可能推理过程一团糟——建议点开看看你的模型在哪个维度翻车。

AI 摘要

当前LLM评估主要依赖最终答案正确率,忽略了推理过程的质量。本研究提出一个多维度行为框架,从正确性、一致性、鲁棒性、逻辑连贯性、效率和稳定性六个维度衡量推理质量。实验发现,逻辑连贯性与正确性正交(r=-0.172),即正确答案可能来自不连贯推理。该框架还暴露了排名反转:DeepSeek-V3在准确率优先下排名第二,但在法律/合规权重下排名第五。该框架为模型部署决策提供了更全面的信号,特别适用于需要审计推理过程的场景。

原文 · arXiv: DeepSeek

Measuring Reasoning Quality in LLMs: A Multi-Dimensional Behavioral Framework

LLMs have achieved remarkable success in complex reasoning tasks, yet current evaluation approaches predominantly rely on final-answer correctness, offering limited insight into the underlying reasoning processes that produce those answers. To address this gap, this study proposes a unified multi-dimensional framework for measuring reasoning quality in LLMs from a behavioral perspective, operationalizing six theoretically grounded dimensions: Correctness (CQ), Consistency (CS), Robustness (RS), Logical Coherence (LS), Efficiency (ES), and Stability (SS). Extensive experiments on seven LLMs across 975 items from four benchmarks demonstrate that the framework reveals behaviors invisible to accuracy-only metrics. Notably, logical coherence is orthogonal to correctness (r = -0.172, ns), confirming that correct answers can arise from incoherent reasoning, while Claude-Haiku-4.5 achieves the highest multi-dimensional score (Q_bal = 0.778). Furthermore, the framework exposes critical ranking inversions: DeepSeek-V3 ranks second under accuracy-priority but fifth under legal/compliance weighting, a reversal that single-metric evaluation cannot detect. Discriminant validity confirms 11/15 dimension pairs are independent (|r| < 0.50), providing psychometric support for treating each dimension as a distinct signal. The dimensional profiles produced by the framework directly support three classes of deployment decision: identifying models whose reasoning traces would fail accountability audits despite correct final answers (LS--CQ orthogonality); preventing ranking errors caused by accuracy-only benchmarking; and ensuring that no single metric silently substitutes for the six independent signals the framework captures.

LLM推理质量评估新框架:多维度行为框架揭示准确率之外的盲区 · AI 热点