用大模型帮印度消费者一键解决投诉难题
$\textit{Grahak-Nyay:}$ Consumer Grievance Redressal through Large Language Models
- 基于开源大模型和检索增强生成，简化法律流程
- 构建三类新数据集，支持准确快速响应
- 专为法律专家验证，适合普通用户使用
印度消费者维权常因程序复杂、法律术语难懂及管辖权问题受阻。为此，我们提出Grahak-Nyay（消费者正义），一个基于开源大语言模型（LLMs）与检索增强生成（RAG）的聊天机器人，通过简洁更新的知识库化解法律复杂性。我们构建了三个新数据集：GeneralQA（通用消费法）、SectoralQA（行业专项知识）和SyntheticQA（用于RAG评估），以及包含300条标注对话的NyayChat数据集。同时引入来自印度消费者法院的判决数据（Judgments），以支持决策并增强用户信任。我们提出HAB评价指标（帮助性、准确性、简洁性）评估性能，法律专家已验证其有效性。代码与数据集将公开。
原文摘要 · Abstract (English)
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.
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