Meta 与密歇根州立大学论文：LLM 推理图质量参差，错误会传导至置信度分数

Rohan Paul· @rohanpaul_ai · X·2026-10-22 13:27· 47 分钟前AI 评分43

AI 导读

Meta 与密歇根州立大学的新论文发现，LLM 构建的推理图质量差异很大，且图中的错误会传导到基于图计算的置信度分数上。研究测试了 9 个模型，从给定步骤到完全自建图逐步减少辅助，结果显示图质量更多与模型家族和图结构相关，而非模型规模，Qwen3-32B 回答子问题的表现甚至不如 Qwen3-8B。作者建议在信任基于 LLM 推理图得出的答案或置信度之前，先检查图本身。

正文 · 原文

New Meta + Michigan State Univ paper finds that LLM-built reasoning graphs vary widely in quality, and their errors carry into the confidence scores computed from them.

Many methods split a question into a graph of sub-questions, then compute answers or confidence on top. They assume the LLM's graph is correct, which is rarely checked.

They tested 9 models with less and less help, from wiring given steps to building the whole graph. Quality tracked model family and graph shape more than size, and Qwen3-32B answered sub-questions worse than Qwen3-8B.

If you build on LLM-made reasoning graphs, check the graph itself before trusting answers or confidence computed from it.

来源：Rohan Paul · x.com