面向多轮指令对话的归纳-演绎策略复用
Inductive-Deductive Strategy Reuse for Multi-Turn Instructional Dialogues
AI总结：
本文提出归纳-演绎策略复用方法，从真实对话中归纳高层指令策略并演绎应用于新场景，以生成多样深入的多轮指令对话，提升下游聊天模型性能。
摘要：
使大语言模型（LLMs）与人类期望对齐需要高质量的指令对话，这通常要求指令具有多样性和深度。现有方法利用两个LLM进行交互以自动收集数据：一个模拟用户提出指令，另一个充当系统代理作出回应。然而，这些用户模拟器在缺乏明确引导的情况下难以建模对话如何提出不同指令背后的规则，导致生成的指令较为笼统。本文提出显式捕捉复杂规则，以帮助用户模拟器提出多样且深入的指令。具体而言，我们首先从各种真实指令对话中归纳出高层指令策略，将其作为规则。随后，将不同的可能策略以演绎方式应用于新给定的对话场景，以提出各种指令。实验结果表明，我们的方法能够生成多样且深入的指令。所构建的多轮指令对话在下游聊天模型上能够优于竞争基线。
英文摘要：
Aligning large language models (LLMs) with human expectations requires high-quality instructional dialogues, which usually require instructions that are diverse and in-depth. Existing methods leverage two LLMs to interact for automatic collection: one simulating a user to pose instructions, and the other acting as a system agent to respond. However, these user simulators struggle to model the rules behind how dialogues can pose different instructions without explicit guidance, resulting in general instructions. In this paper, we propose to explicitly capture the complex rules to help the user simulator pose diverse and in-depth instruction. Specifically, we first induce high-level instruction strategies from various real instruction dialogues serving as rules. Afterward, different possible strategies are applied to the newly given dialogue scenario deductively to pose various instructions. Experimental results show that our method can generate diverse and in-depth instructions. The constructed multi-turn instructional dialogues can outperform competitive baselines on the downstream chat model.
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。
