提出双向可理解协议，提升人与大模型多轮交互效率。
Multi-Turn Human-LLM Interaction Through the Lens of a Two-Way Intelligibility Protocol
- 用有限状态机建模人机双向理解过程
- 在放射科与药物设计中验证协议有效性
- 适合需要深度协作的人机系统设计
本文研究人类专家通过自然语言与大语言模型（LLM）在数据分析任务中的多轮交互。针对复杂问题，探索利用人类专业知识与创造力协同求解的可能。基于文献[3]提出的智能体间交互协议，本文提出以“双向可理解性”为核心的结构化交互框架，并以一对通信的有限状态机建模。实现该协议后，在放射学和药物设计两个科学领域进行实验：一是使用数据库作为人类代理的受控实验，二是真实人类参与的非受控实验。结果表明，该协议能有效捕捉人-模型交互中的一向与双向可理解性，证实双向可理解性在人机系统设计中的实用价值。代码已开源于https://github.com/karannb/interact。
原文摘要 · Abstract (English)
Our interest is in the design of software systems involving a human-expert interacting -- using natural language -- with a large language model (LLM) on data analysis tasks. For complex problems, it is possible that LLMs can harness human expertise and creativity to find solutions that were otherwise elusive. On one level, this interaction takes place through multiple turns of prompts from the human and responses from the LLM. Here we investigate a more structured approach based on an abstract protocol described in [3] for interaction between agents. The protocol is motivated by a notion of "two-way intelligibility" and is modelled by a pair of communicating finite-state machines. We provide an implementation of the protocol, and provide empirical evidence of using the implementation to mediate interactions between an LLM and a human-agent in two areas of scientific interest (radiology and drug design). We conduct controlled experiments with a human proxy (a database), and uncontrolled experiments with human subjects. The results provide evidence in support of the protocol's capability of capturing one- and two-way intelligibility in human-LLM interaction; and for the utility of two-way intelligibility in the design of human-machine systems. Our code is available at https://github.com/karannb/interact.
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