ESG-Bench: 对长上下文 ESG 报告进行基准测试以缓解幻觉
ESG-Bench: Benchmarking Long-Context ESG Reports for Hallucination Mitigation
AI总结：
本文提出 ESG-Bench 数据集，用于评估大语言模型在解析 ESG 报告和减少幻觉方面的能力，通过人工标注的 QA 对和细粒度标签，系统评估模型提取和推理 ESG 内容的能力。
摘要：
随着企业责任越来越多地纳入环境、社会和治理（ESG）标准，ESG 报告已成为许多地区法律要求的关键渠道，用于记录可持续实践并评估企业的长期和道德表现。然而，ESG 披露的长度和复杂性使它们难以解读和可靠地自动化分析。为支持可扩展和可信的分析，本文介绍 ESG-Bench，一个用于 ESG 报告理解和缓解大语言模型（LLM）幻觉的基准数据集。ESG-Bench 包含人工标注的基于真实世界 ESG 报告上下文的问题-答案（QA）对，细粒度标签表明模型输出是否被事实支持或产生幻觉。将 ESG 报告分析框架为具有可验证性约束的 QA 任务，使系统评估 LLMs 提取和推理 ESG 内容的能力，并提供新的应用场景：在社会敏感、合规关键的设置中缓解幻觉。我们设计了任务特定的链式思考（CoT）提示策略，并使用 CoT-标注的推理进行微调多个最先进的 LLMs。我们的实验表明，这些基于 CoT 的方法在减少幻觉方面显著优于标准提示和直接微调，并且这些收益转移到了超出 ESG 领域的现有 QA 基准测试中。
英文摘要：
As corporate responsibility increasingly incorporates environmental, social, and governance (ESG) criteria, ESG reporting is becoming a legal requirement in many regions and a key channel for documenting sustainability practices and assessing firms' long-term and ethical performance. However, the length and complexity of ESG disclosures make them difficult to interpret and automate the analysis reliably. To support scalable and trustworthy analysis, this paper introduces ESG-Bench, a benchmark dataset for ESG report understanding and hallucination mitigation in large language models (LLMs). ESG-Bench contains human-annotated question-answer (QA) pairs grounded in real-world ESG report contexts, with fine-grained labels indicating whether model outputs are factually supported or hallucinated. Framing ESG report analysis as a QA task with verifiability constraints enables systematic evaluation of LLMs' ability to extract and reason over ESG content and provides a new use case: mitigating hallucinations in socially sensitive, compliance-critical settings. We design task-specific Chain-of-Thought (CoT) prompting strategies and fine-tune multiple state-of-the-art LLMs on ESG-Bench using CoT-annotated rationales. Our experiments show that these CoT-based methods substantially outperform standard prompting and direct fine-tuning in reducing hallucinations, and that the gains transfer to existing QA benchmarks beyond the ESG domain.
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。
