梳理人与大模型协作的原理与挑战，助力高效智能协作
How to Enable Effective Cooperation Between Humans and NLP Models: A Survey of Principles, Formalizations, and Beyond
- 提出统一分类框架，系统归纳人机协作方法
- 梳理协作中的核心原则与形式化理论
- 适合关注AI交互、人机协同的研究者
随着大语言模型（LLMs）的发展，智能模型已从工具演变为具有自主目标和协作策略的代理。这一转变催生了自然语言处理中的人-模型协作新范式，近年来在众多任务中取得显著进展。本文首次系统综述人-模型协作，探讨其基本原则、形式化方法及开放挑战。我们提出一种新分类体系，提供统一视角总结现有方法，并讨论潜在前沿方向及其对应难题。本工作旨在作为入门指引，推动该领域进一步突破。
原文摘要 · Abstract (English)
With the advancement of large language models (LLMs), intelligent models have evolved from mere tools to autonomous agents with their own goals and strategies for cooperating with humans. This evolution has birthed a novel paradigm in NLP, i.e., human-model cooperation, that has yielded remarkable progress in numerous NLP tasks in recent years. In this paper, we take the first step to present a thorough review of human-model cooperation, exploring its principles, formalizations, and open challenges. In particular, we introduce a new taxonomy that provides a unified perspective to summarize existing approaches. Also, we discuss potential frontier areas and their corresponding challenges. We regard our work as an entry point, paving the way for more breakthrough research in this regard.
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