Is There No Such Thing as a Bad Question? H4R: HalluciBot For Ratiocination, Rewriting, Ranking, and Routing

📄 arXiv: 2404.12535v3 📥 PDF

作者: William Watson, Nicole Cho, Nishan Srishankar

分类: cs.LG, cs.AI, cs.CL

发布日期: 2024-04-18 (更新: 2024-12-16)

备注: Accepted at The 39th Annual AAAI Conference on Artificial Intelligence (AAAI 2025)

💡 一句话要点

提出HalluciBot以解决大语言模型中的幻觉问题

🎯 匹配领域: 支柱九：具身大模型 (Embodied Foundation Models)

关键词: 大型语言模型 查询优化 幻觉现象 HalluciBot 多项选择题

📋 核心要点

  1. 现有方法主要关注生成后分析，未能有效评估查询质量，导致幻觉现象频发。
  2. HalluciBot通过在生成前评估查询的幻觉倾向，提供了一种新的查询优化方法，避免了对LLMs的依赖。
  3. 实验结果显示，HalluciBot在多项选择题上实现了95.7%的输出准确率，显著提升了查询的有效性。

📝 摘要（中文）

幻觉现象仍然是大型语言模型（LLMs）在机构采用过程中面临的关键挑战之一。尽管以往研究主要集中在生成后输出的分析和优化上，本文则关注于查询在引导LLMs生成准确响应中的有效性。我们提出了HalluciBot模型，该模型在生成前评估查询的幻觉倾向，而无需在推理过程中调用任何LLMs。HalluciBot可以作为查询重写的代理奖励模型，提供基于准确性和共识的查询质量评估框架。通过HalluciBot的经验估计指导的查询重写，我们展示了多项选择题的输出准确率可达到95.7%。

🔬 方法详解

问题定义：本文旨在解决大型语言模型生成过程中查询质量不足导致的幻觉现象。现有方法多集中于生成后的输出优化，未能有效评估输入查询的质量，造成生成结果的不准确性。

核心思路：HalluciBot的核心思路是通过评估查询的幻觉倾向，提前识别潜在的低质量查询，从而优化生成过程。该方法不依赖于LLMs的实时调用，降低了计算成本。

技术框架：HalluciBot的整体架构包括查询扰动、输出采样、多代理蒙特卡洛模拟和编码器分类器训练等模块。具体流程为：对369,837个查询进行n次扰动，利用n+1个独立的LLM代理进行输出采样，随后进行模拟和分类器训练。

关键创新：HalluciBot的主要创新在于其通过查询扰动技术提升了输出多样性，并实现了对查询质量的有效评估。这一方法与传统的后处理优化方法本质上不同，前者在生成前就能识别问题查询。

关键设计：在训练过程中，采用了多种扰动策略以增加输出的多样性，实验表明通过这种方式可以提高输出的一致性（+12.5%的一致性扩展）。此外，HalluciBot在处理幻觉查询时，测试F1分数达76.0%，计算节省达46.6%。

🖼️ 关键图片

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📊 实验亮点

实验结果显示，HalluciBot在多项选择题的输出准确率达到了95.7%，相比于传统方法，正类转变率提升了30.2%，并且在幻觉查询的处理上节省了46.6%的计算资源，展示了其显著的性能优势。

🎯 应用场景

HalluciBot的研究成果在多个领域具有潜在应用价值，尤其是在教育、客服和信息检索等需要高准确性响应的场景中。通过优化查询质量，该模型能够显著提升用户体验和信息获取效率，未来可能推动更广泛的LLM应用。

📄 摘要（原文）

Hallucination continues to be one of the most critical challenges in the institutional adoption journey of Large Language Models (LLMs). While prior studies have primarily focused on the post-generation analysis and refinement of outputs, this paper centers on the effectiveness of queries in eliciting accurate responses from LLMs. We present HalluciBot, a model that estimates the query's propensity to hallucinate before generation, without invoking any LLMs during inference. HalluciBot can serve as a proxy reward model for query rewriting, offering a general framework to estimate query quality based on accuracy and consensus. In essence, HalluciBot investigates how poorly constructed queries can lead to erroneous outputs - moreover, by employing query rewriting guided by HalluciBot's empirical estimates, we demonstrate that 95.7% output accuracy can be achieved for Multiple Choice questions. The training procedure for HalluciBot consists of perturbing 369,837 queries n times, employing n+1 independent LLM agents, sampling an output from each query, conducting a Multi-Agent Monte Carlo simulation on the sampled outputs, and training an encoder classifier. The idea of perturbation is the outcome of our ablation studies that measures the increase in output diversity (+12.5 agreement spread) by perturbing a query in lexically different but semantically similar ways. Therefore, HalluciBot paves the way to ratiocinate (76.0% test F1 score, 46.6% in saved computation on hallucinatory queries), rewrite (+30.2% positive class transition from hallucinatory to non-hallucinatory), rank (+50.6% positive class transition from hallucinatory to non-hallucinatory), and route queries to effective pipelines.