PEFT能提升幻觉检测能力，让模型更清楚自己何时在胡说。
Small Updates, Big Doubts: Does Parameter-Efficient Fine-tuning Enhance Hallucination Detection ?
- 用七种无监督方法测试三种LLM在三类问答数据集上的幻觉检测表现。
- PEFT使幻觉检测的AUROC显著提升，尤其在语义一致性类方法中效果明显。
- 提升关键在于调整模型对不确定性的表达方式，而非注入新知识。
参数高效微调（PEFT）方法被广泛用于将大语言模型（LLMs）适配到下游任务，常被认为能提升事实正确性。然而，其对幻觉行为的影响仍不明确，尤其是在面向事实查询的问答数据集上。本文通过跨三个开源权重大模型和三个事实导向问答基准的系统性实证研究，全面考察了PEFT对幻觉检测的影响。针对每个模型，我们采用七种无监督幻觉检测方法评估性能，涵盖三类互补方法：基于语义一致性的检测器、基于置信度的检测器和基于熵的检测器。多维度评估使我们能够刻画PEFT如何重塑不同检测范式下的不确定性表达。实验结果表明，PEFT始终增强幻觉检测能力，在多种检测器上显著提升AUROC。进一步的线性探测与表征诊断分析显示，PEFT主要通过重构不确定性编码与呈现方式起作用，而非向模型注入新事实知识。
原文摘要 · Abstract (English)
Parameter-efficient fine-tuning (PEFT) methods are widely used to adapt large language models (LLMs) to downstream tasks and are often assumed to improve factual correctness. However, how the parameter-efficient fine-tuning methods affect hallucination behavior remains insufficiently understood, especially on QA datasets. In this work, we systematically investigate the impact of PEFT on hallucination detection through a comprehensive empirical study across three open-weight LLM backbones and three fact-seeking QA benchmarks. For each model, we evaluate performance using seven unsupervised hallucination detection methods spanning three complementary approaches: semantic consistency based detectors, confidence based detectors, and entropy based detectors. This multifaceted evaluation enables us to characterize how PEFT reshapes uncertainty across different detection paradigms. In conclusion, our experimental results show that PEFT consistently strengthens hallucination detection ability, substantially improving AUROC across a wide range of hallucination detectors. Besides, further analyses using linear probes and representation diagnostics indicate that PEFT methods primarily reshapes how uncertainty is encoded and surfaced, comparing with injecting new factual knowledge into the models.
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