$\textit{Grahak-Nyay:}$ 通过大型语言模型进行消费者投诉解决
$\textit{Grahak-Nyay:}$ Consumer Grievance Redressal through Large Language Models
AI总结：
针对印度消费者投诉解决因程序复杂等受阻的问题，提出基于开源LLM和RAG的Grahak-Nyay聊天机器人，构建新数据集并设计HAB评估指标，经专家验证有效，旨在简化消费者维权流程。
摘要：
在印度，消费者投诉解决的获取往往受到程序复杂性、法律术语和管辖权挑战的阻碍。为此，我们提出$\textbf{Grahak-Nyay}$（消费者正义），这是一款使用开源大型语言模型（LLMs）和检索增强生成（RAG）简化流程的聊天机器人。Grahak-Nyay通过简洁且最新的知识库简化法律复杂性。我们引入三个新数据集：$\textit{GeneralQA}$（一般消费者法）、$\textit{SectoralQA}$（特定行业知识）和$\textit{SyntheticQA}$（用于RAG评估），以及$\textit{NyayChat}$（含300个标注的聊天机器人对话）。我们还引入源自印度消费者法院的$\textit{Judgments}$数据，以辅助聊天机器人决策并增强用户信任。我们提出$\textbf{HAB}$指标（有用性、准确性、简洁性）评估聊天机器人性能。法律领域专家验证了Grahak-Nyay的有效性。代码和数据集将发布。
英文摘要：
Access to consumer grievance redressal in India is often hindered by procedural complexity, legal jargon, and jurisdictional challenges. To address this, we present $\textbf{Grahak-Nyay}$ (Justice-to-Consumers), a chatbot that streamlines the process using open-source Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG). Grahak-Nyay simplifies legal complexities through a concise and up-to-date knowledge base. We introduce three novel datasets: $\textit{GeneralQA}$ (general consumer law), $\textit{SectoralQA}$ (sector-specific knowledge) and $\textit{SyntheticQA}$ (for RAG evaluation), along with $\textit{NyayChat}$, a dataset of 300 annotated chatbot conversations. We also introduce $\textit{Judgments}$ data sourced from Indian Consumer Courts to aid the chatbot in decision making and to enhance user trust. We also propose $\textbf{HAB}$ metrics ($\textbf{Helpfulness, Accuracy, Brevity}$) to evaluate chatbot performance. Legal domain experts validated Grahak-Nyay's effectiveness. Code and datasets will be released.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
