定制化大模型聊天机器人助力行为科学实验研究
Customizable LLM-Powered Chatbot for Behavioral Science Research
- 基于LLM构建可配置的网页聊天系统，专为科研实验设计
- 支持自定义事件与日志，实现数据精准追踪与交叉验证
- 适用于行为科学，亦可拓展至信息检索等交互研究
人工智能的快速发展催生了能够生成类人文本的大语言模型（LLMs），这些模型已广泛应用于各类交互场景。本研究提出一种定制化大模型驱动的聊天机器人（CLPC），这是一个面向行为科学实验设计的网页系统。用户需输入用户名和实验代码方可访问，确保数据可追溯、可交叉验证，提升研究数据的完整性与可靠性。系统具备灵活扩展性，可轻松添加新事件类型，支持研究人员集成自定义日志，无需额外开发日志机制。尽管主要面向行为科学，该系统亦可适配信息检索研究或一般聊天机器人交互实验。
原文摘要 · Abstract (English)
The rapid advancement of Artificial Intelligence has resulted in the advent of Large Language Models (LLMs) with the capacity to produce text that closely resembles human communication. These models have been seamlessly integrated into diverse applications, enabling interactive and responsive communication across multiple platforms. The potential utility of chatbots transcends these traditional applications, particularly in research contexts, wherein they can offer valuable insights and facilitate the design of innovative experiments. In this study, we present a Customizable LLM-Powered Chatbot (CLPC), a web-based chatbot system designed to assist in behavioral science research. The system is meticulously designed to function as an experimental instrument rather than a conventional chatbot, necessitating users to input a username and experiment code upon access. This setup facilitates precise data cross-referencing, thereby augmenting the integrity and applicability of the data collected for research purposes. It can be easily expanded to accommodate new basic events as needed; and it allows researchers to integrate their own logging events without the necessity of implementing a separate logging mechanism. It is worth noting that our system was built to assist primarily behavioral science research but is not limited to it, it can easily be adapted to assist information retrieval research or interacting with chat bot agents in general.
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