LIFT：基于似然的干预忠实度训练，提升 LLM 链式推理忠实性

LIFT: Likelihood-Based Intervention Faithfulness Training

AI 导读

研究者提出 LIFT，通过将推理步骤替换为领域匹配但无关的事实、测量答案对数概率下降来自动为每步推理打因果影响分，并在高分的 CoT 轨迹上微调，同时优化忠实度与准确率。该方法在 Mistral-7B-Instruct、Qwen3-8B 和 Qwen3-4B-Instruct 三个模型及三个基准上提升了忠实度，准确率与仅针对准确率微调的模型相当，且无需人工标注。

正文 · 原文

View PDF HTML (experimental)

Abstract:Chain-of-thought (CoT) reasoning improves LLM accuracy on complex tasks, but CoTs are often unfaithful; that is, the model does not rely on the stated reasoning steps to produce its answer. Existing methods to improve faithfulness require human annotations. We propose LIFT, which automatically scores each reasoning step by its causal influence: we replace it with a domain-matched but irrelevant fact and measure the drop in answer log-probability. We fine-tune on high-scoring CoT traces where interventions successfully disrupted reasoning, jointly optimizing for faithfulness and accuracy. LIFT improves faithfulness across three models (Mistral-7B-Instruct, Qwen3-8B, and Qwen3-4B-Instruct) and three benchmarks, with accuracy comparable to a model fine-tuned solely for accuracy.

Comments:	13 pages, 3 figures
Subjects:	Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as:	arXiv:2509.13334 [cs.AI]
	(or arXiv:2509.13334v2 [cs.AI] for this version)
	https://doi.org/10.48550/arXiv.2509.13334 arXiv-issued DOI via DataCite

Submission history

From: Anand Swaroop [view email]
[v1] Wed, 10 Sep 2025 07:07:17 UTC (22,530 KB)
[v2] Mon, 12 Oct 2026 21:45:58 UTC (84 KB)

来源：arXiv cs.AI · arxiv.org