$\textit{Grahak-Nyay:}$ Consumer Grievance Redressal through Large Language Models
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arXiv
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| Autores principales: | , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866916830105305088 |
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| author | Ganatra, Shrey Bhattacharyya, Swapnil Kashid, Harshvivek Anaokar, Spandan Nair, Shruti Sekhar, Reshma Manohar, Siddharth Hemrajani, Rahul Bhattacharyya, Pushpak |
| author_facet | Ganatra, Shrey Bhattacharyya, Swapnil Kashid, Harshvivek Anaokar, Spandan Nair, Shruti Sekhar, Reshma Manohar, Siddharth Hemrajani, Rahul Bhattacharyya, Pushpak |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2507_04854 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | $\textit{Grahak-Nyay:}$ Consumer Grievance Redressal through Large Language Models Ganatra, Shrey Bhattacharyya, Swapnil Kashid, Harshvivek Anaokar, Spandan Nair, Shruti Sekhar, Reshma Manohar, Siddharth Hemrajani, Rahul Bhattacharyya, Pushpak Computation and Language 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. |
| title | $\textit{Grahak-Nyay:}$ Consumer Grievance Redressal through Large Language Models |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2507.04854 |