{"ID":2897464,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.04854","arxiv_id":"2507.04854","title":"$\\textit{Grahak-Nyay:}$ Consumer Grievance Redressal through Large Language Models","abstract":"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.","short_abstract":"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-Augmente...","url_abs":"https://arxiv.org/abs/2507.04854","url_pdf":"https://arxiv.org/pdf/2507.04854v1","authors":"[\"Shrey Ganatra\",\"Swapnil Bhattacharyya\",\"Harshvivek Kashid\",\"Spandan Anaokar\",\"Shruti Nair\",\"Reshma Sekhar\",\"Siddharth Manohar\",\"Rahul Hemrajani\",\"Pushpak Bhattacharyya\"]","published":"2025-07-07T10:26:42Z","proceeding":"cs.CL","tasks":"[\"cs.CL\"]","methods":"[\"RAG\",\"Large Language Model\",\"Language Model\"]","has_code":false}