@inproceedings{ganatra-etal-2025-grahak,
    title = "Grahak-Nyay: Consumer Grievance Redressal through Large Language Models",
    author = "Ganatra, Shrey  and
      Bhattacharyya, Swapnil  and
      Kashid, Harshvivek  and
      Anaokar, Spandan  and
      Nair, Shruthi N  and
      Sekhar, Reshma  and
      Manohar, Siddharth  and
      Hemrajani, Rahul  and
      Bhattacharyya, Pushpak",
    editor = "Modi, Ashutosh  and
      Ghosh, Saptarshi  and
      Ekbal, Asif  and
      Goyal, Pawan  and
      Jain, Sarika  and
      Joshi, Abhinav  and
      Mishra, Shivani  and
      Datta, Debtanu  and
      Paul, Shounak  and
      Singh, Kshetrimayum Boynao  and
      Kumar, Sandeep",
    booktitle = "Proceedings of the 1st Workshop on NLP for Empowering Justice (JUST-NLP 2025)",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.justnlp-main.7/",
    pages = "53--72",
    ISBN = "979-8-89176-312-8",
    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 303 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 are available at https://github.com/ShreyGanatra/GrahakNyay.git."
}