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How to Enable Effective Cooperation Between Humans and NLP Models: A Survey of Principles, Formalizations, and Beyond

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arxiv 2501.05714 v4 pith:TFVFNBZM submitted 2025-01-10 cs.CL cs.AIcs.HC

classification cs.CLcs.AIcs.HC
keywords cooperationmodelschallengesformalizationshuman-modelhumansprinciplesregard
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 1 Pith paper

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  1. Beyond Solving Math Quiz: Evaluating the Ability of Large Reasoning Models to Ask for Information

    cs.AI 2025-08 unverdicted novelty 5.0 of 10

    Per the abstract, large reasoning models systematically fail to ask for missing information on under-specified math problems, a skill standard benchmarks never test.

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