textitGrahak-Nyay: Consumer Grievance Redressal through Large Language Models

Access to consumer grievance redressal in India is often hindered by procedural complexity, legal jargon, and jurisdictional challenges. To address this, we present 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: textitGeneralQA (general consumer law), textitSectoralQA (sector-specific knowledge) and textitSyntheticQA (for RAG evaluation), along with textitNyayChat, a dataset of 300 annotated chatbot conversations. We also introduce textitJudgments data sourced from Indian Consumer Courts to aid the chatbot in decision making and to enhance user trust. We also propose HAB metrics (Helpfulness, Accuracy, Brevity) to evaluate chatbot performance. Legal domain experts validated Grahak-Nyay's effectiveness. Code and datasets will be released.