@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://aclanthology.org/2025.justnlp-main.7/",
doi = "10.18653/v1/2025.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."
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<abstract>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: GeneralQA (general consumer law), SectoralQA (sector-specific knowledge) and SyntheticQA (for RAG evaluation), along with NyayChat, a dataset of 303 annotated chatbot conversations. We also introduce Judgments 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 are available at https://github.com/ShreyGanatra/GrahakNyay.git.</abstract>
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%0 Conference Proceedings
%T Grahak-Nyay: Consumer Grievance Redressal through Large Language Models
%A Ganatra, Shrey
%A Bhattacharyya, Swapnil
%A Kashid, Harshvivek
%A Anaokar, Spandan
%A Nair, Shruthi N.
%A Sekhar, Reshma
%A Manohar, Siddharth
%A Hemrajani, Rahul
%A Bhattacharyya, Pushpak
%Y Modi, Ashutosh
%Y Ghosh, Saptarshi
%Y Ekbal, Asif
%Y Goyal, Pawan
%Y Jain, Sarika
%Y Joshi, Abhinav
%Y Mishra, Shivani
%Y Datta, Debtanu
%Y Paul, Shounak
%Y Singh, Kshetrimayum Boynao
%Y Kumar, Sandeep
%S Proceedings of the 1st Workshop on NLP for Empowering Justice (JUST-NLP 2025)
%D 2025
%8 December
%I Association for Computational Linguistics
%C Mumbai, India
%@ 979-8-89176-312-8
%F ganatra-etal-2025-grahak
%X 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: GeneralQA (general consumer law), SectoralQA (sector-specific knowledge) and SyntheticQA (for RAG evaluation), along with NyayChat, a dataset of 303 annotated chatbot conversations. We also introduce Judgments 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 are available at https://github.com/ShreyGanatra/GrahakNyay.git.
%R 10.18653/v1/2025.justnlp-main.7
%U https://aclanthology.org/2025.justnlp-main.7/
%U https://doi.org/10.18653/v1/2025.justnlp-main.7
%P 53-72
Markdown (Informal)
[Grahak-Nyay: Consumer Grievance Redressal through Large Language Models](https://aclanthology.org/2025.justnlp-main.7/) (Ganatra et al., JUSTNLP 2025)
ACL
- Shrey Ganatra, Swapnil Bhattacharyya, Harshvivek Kashid, Spandan Anaokar, Shruthi N Nair, Reshma Sekhar, Siddharth Manohar, Rahul Hemrajani, and Pushpak Bhattacharyya. 2025. Grahak-Nyay: Consumer Grievance Redressal through Large Language Models. In Proceedings of the 1st Workshop on NLP for Empowering Justice (JUST-NLP 2025), pages 53–72, Mumbai, India. Association for Computational Linguistics.