诊断与改进大语言模型的概率推理：一项决策论框架研究
Diagnosing and Improving Probabilistic Reasoning in Large Language Models
AI 导读
一项 arXiv 研究提出决策论框架，将 LLM 的决策损失分解为从证据形成准确信念和依据效用函数转化为行动两个部分，并在有已知真值的合成基准上评估前沿与开源模型。研究发现针对单一组件的 RL 干预会重新分配决策损失、改进目标但不必然迁移，联合训练信念形成与决策可同时改进两者，但依赖训练与评估格式匹配。
正文 · 原文
Abstract:Large language models (LLMs) are increasingly proposed as decision assistants who must reason probabilistically from available evidence under explicit decision costs. We propose a decision-theoretic framework that decomposes LLMs' decision loss into two components: forming accurate beliefs from provided evidence and translating those beliefs into actions that optimize a provided utility function. Using a synthetic benchmark with known ground truth, we apply the decomposition to characterize probabilistic reasoning in frontier and open-sourced models. We further evaluate whether RL interventions targeting beliefs, decisions, or both improve these components across three domains, whether improvements transfer across components and elicitation formats, and whether decision performance can improve without improvement in belief formation. We find that targeting one component of probabilistic reasoning redistributes decision loss, improving the target without necessarily transferring to others, and that jointly targeting belief formation and decision-making improves both but hinges on matched formats between training and evaluation.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2609.38005 [cs.AI] |
| (or arXiv:2609.38005v3 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2609.38005 arXiv-issued DOI via DataCite |
Submission history
From: Huaman Sun [view email]
[v1]
Tue, 29 Sep 2026 16:55:50 UTC (508 KB)
[v2]
Tue, 6 Oct 2026 18:09:00 UTC (501 KB)
[v3]
Thu, 5 Nov 2026 02:21:56 UTC (515 KB)
来源：arXiv cs.AI · arxiv.org