DeepThink：使语言模型与特定领域用户意图对齐
DeepThink: Aligning Language Models with Domain-Specific User Intents
AI总结：
针对合成指令与真实用户需求偏差的问题，本研究提出DeepThink框架，通过生成种子问题、模拟对话挖掘需求、结合上下文和检索文档优化答案，在广告领域真实测试集上较GPT-4-turbo+RAG助手平均提升7.92%性能。
摘要：
利用合成指令进行监督微调是将大语言模型（LLMs）适配到特定领域问答任务的常用方法。然而，合成指令与真实用户问题及预期答案存在偏差。本研究提出名为DeepThink的新型框架，用于生成高质量指令。DeepThink首先生成少量种子问题以模拟真实用户问题，通过模拟对话揭示隐藏的用户需求，并结合对话上下文和检索到的文档优化答案，以获得更全面的回答。实验表明，在广告领域的真实用户测试集上，DeepThink相比基于GPT-4-turbo+RAG的助手，在相关性、完整性、清晰度、准确性和可操作性等维度上平均性能提升7.92%。
英文摘要：
Supervised fine-tuning with synthesized instructions has been a common practice for adapting LLMs to domain-specific QA tasks. However, the synthesized instructions deviate from real user questions and expected answers. This study proposes a novel framework called DeepThink to generate high-quality instructions. DeepThink first generates a few seed questions to mimic actual user questions, simulates conversations to uncover the hidden user needs, and refines the answer by conversational contexts and the retrieved documents for more comprehensive answers. Experiments demonstrate that DeepThink achieves an average performance improvement of 7.92% compared to a GPT-4-turbo+RAG-based assistant on the real user test set in the advertising domain across dimensions such as relevance, completeness, clarity, accuracy, and actionability.
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。
