倾听上下文：面向气候问题检索增强生成的忠实大型语言模型
Listen to the Context: Towards Faithful Large Language Models for Retrieval Augmented Generation on Climate Questions
AI总结：
针对气候问题检索增强生成中模型输出不忠实的问题，本文提出自动评估方法，并通过排除不忠实训练数据开发ClimateGPT Faithful+，将忠实度从30%提升至57%。
摘要：
使用检索增强生成的大型语言模型，通过使冗长且技术性的气候相关文档更易于理解，有潜力为研究人员、政策制定者和公众解锁宝贵知识。虽然这种方法可以通过依赖检索到的段落作为额外上下文来帮助缓解事实性幻觉，但其有效性取决于模型的输出是否忠实于这些段落。为解决这一问题，我们探索了在此设置下对不同模型忠实度的自动评估。随后，我们聚焦于ClimateGPT，一个专门研究气候科学的大型语言模型，以考察其指令微调中的哪些因素影响模型的忠实度。通过排除模型训练数据中不忠实的子集，我们开发了ClimateGPT Faithful+，根据我们的自动指标，其在支持的原子声明上的忠实度从30%提升到57%。
英文摘要：
Large language models that use retrieval augmented generation have the potential to unlock valuable knowledge for researchers, policymakers, and the public by making long and technical climate-related documents more accessible. While this approach can help alleviate factual hallucinations by relying on retrieved passages as additional context, its effectiveness depends on whether the model's output remains faithful to these passages. To address this, we explore the automatic assessment of faithfulness of different models in this setting. We then focus on ClimateGPT, a large language model specialised in climate science, to examine which factors in its instruction fine-tuning impact the model's faithfulness. By excluding unfaithful subsets of the model's training data, we develop ClimateGPT Faithful+, which achieves an improvement in faithfulness from 30% to 57% in supported atomic claims according to our automatic metric.
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。
