迈向轻量级可靠性：使用软提示缓解大型语言模型中的幻觉
Towards Lightweight Reliability: Using Soft Prompts for Hallucination Mitigation in Large Language Models
AI总结：
提出一种参数高效的软提示方法RCSP，通过对比学习、课程学习和KL正则化平衡事实回忆、幻觉抑制和弃权，在多个QA数据集上优于基线。
摘要：
大型语言模型（LLMs）已在各个领域得到广泛应用，但其可靠性常因幻觉——听起来合理但事实不正确的回答——而受到损害。在高风险领域，这些错误会降低信任并引入现实风险。为解决这一挑战，我们提出一种参数高效的方法，使用软提示来缓解幻觉内容并促进生成式问答（QA）任务中的负责任弃权。我们的方法称为负责任对比软提示（RCSP），使用复合损失训练软提示，以平衡三个目标：抑制幻觉内容、鼓励在不确定性下弃权、以及保持或改善事实回忆。为实现这些目标，我们在训练机制中融入对比损失、课程学习和KL正则化。我们使用LLM-as-a-Judge框架在五个不同的生成式QA数据集上评估我们的方法。在Gemma 3（12B）和Llama 3.1（8B）骨干上的实验结果表明，RCSP有效平衡了事实回忆与幻觉抑制和弃权，在F分数上通常优于标准推理和基于指令的提示基线。值得注意的是，这些改进仅通过训练其他调优技术所需参数的一小部分实现。我们的结果表明，软提示提供了一条模块化且计算高效的路径，用于提高LLM的可靠性。
英文摘要：
Large language models (LLMs) have seen widespread adoption across various domains, yet their reliability is frequently undermined by hallucinations - responses that are plausible-sounding but factually incorrect. In high-stakes domains, these errors can reduce trust and introduce real-world risk. To address this challenge, we present a parameter-efficient approach that uses soft prompts to mitigate hallucinated content and promote responsible abstention in generative question-answering (QA) tasks. Our method, called Responsible Contrastive Soft Prompting (RCSP), uses a composite loss to train soft prompts that balance three goals: suppressing hallucinatory content, encouraging abstention under uncertainty, and preserving or improving factual recall. To achieve these goals, we incorporate contrastive loss, curriculum learning, and KL regularization into our training mechanism. We evaluate our approach on five diverse generative QA datasets using an LLM-as-a-Judge framework. Experimental results on the Gemma 3 (12B) and Llama 3.1 (8B) backbones demonstrate that RCSP effectively balances factual recall with hallucination suppression and abstention, yielding a generally superior F-score over standard reasoning and instruction-based prompting baselines. Notably, these improvements are achieved by training only a fraction of the parameters required by other tuning techniques. Our results demonstrate that soft prompts provide a modular and computationally efficient path toward improving LLM reliability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
