发表机构
Situated Grounding and Natural Language (SIGNAL) Lab（情境基础与自然语言（SIGNAL）实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究大语言模型在多方协作任务部分信息条件下的语用推理能力，形式化协作认知不对称概念，评估其作为说话者和倾听者合作能力，发现虽有一定语用能力但信息不完整时仍有挑战，部分失败模式与格赖斯准则违反有关。
AI 中文摘要
富有成效的合作依赖于合作性沟通，包括纳入上下文线索进行推理。大语言模型在协作和智能体流程中的使用增加，引发了关于它们在多大程度上展现这些语用能力的问题，特别是在它们可能无法获取与合作者相同信息的场景中。本文在部分信息条件下的多方协作任务中，对大语言模型的语用推理能力进行了新的研究。我们形式化了一种协作认知不对称的概念，将客观任务成功与格赖斯合作原则明确联系起来，并通过实证评估了各种大语言模型作为说话者和倾听者进行合作的能力，包括提示和训练后策略。结果表明，虽然大语言模型在协作环境中展现出一定语用能力，且可通过提示和训练后策略引发，但在信息不完整的语用沟通中仍面临挑战，某些失败模式与未被识别的格赖斯准则违反相关。
英文摘要
Fruitful collaborations rely on cooperative communications, including of contextual cues to incorporate into reasoning. The increasing use of LLMs in collaborative and agentic pipelines raises questions about the extent to which they exhibit these pragmatic capabilities, especially in scenarios where they may not have access to the same information as their collaborators. In this paper, we perform a novel investigation into the pragmatic reasoning capabilities of LLMs in a multi-party collaborative task under partial information conditions. We formalize a notion of collaborative epistemic asymmetry that explicitly connects objective task success to Grice's cooperative principle and empirically assess various LLMs' abilities to act cooperatively as both speakers and listeners, including both prompting and post-training strategies. Our results show that while LLMs exhibit certain pragmatic capabilities in collaborative settings, and these can be elicited through prompting and post-training, they still face challenges in pragmatic communication with incomplete information, and that certain failure modes do correlate with floutings of Grice's maxims that go unrecognized.
