@inproceedings{quinn-etal-2025-regulatory,
title = "Regulatory Question-Answering using Generative {AI}",
author = "Quinn, Devin and
Pai, Sumit P. and
Yousfi, Iman and
Pudota, Nirmala and
Bhattacharya, Sanmitra",
editor = "Gokhan, Tuba and
Wang, Kexin and
Gurevych, Iryna and
Briscoe, Ted",
booktitle = "Proceedings of the 1st Regulatory NLP Workshop (RegNLP 2025)",
month = jan,
year = "2025",
address = "Abu Dhabi, UAE",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.regnlp-1.16/",
pages = "102--106",
abstract = "Although retrieval augmented generation (RAG) has proven to be an effective approach for creating question-answering systems on a corpus of documents, there is a need to improve the performance of these systems, especially in the regulatory domain where clear and accurate answers are required. This paper outlines the methodology used in our submission to the Regulatory Information Retrieval and Answer Generation (RIRAG) shared task at the Regulatory Natural Language Processing Workshop (RegNLP 2025). The goal is to improve document retrieval (Shared Task 1) and answer generation (Shared Task 2). Our pipeline is constructed as a two-step process for Shared Task 1. In the first step, we utilize a text-embedding-ada-002-based retriever, followed by a RankGPT-based re-ranker. The ranked results of Task 1 are then used to generate responses to user queries in Shared Task 2 through a prompt-based approach using GPT-4o. For Shared Task 1, we achieved a recall rate of 75{\%}, and with the prompts we developed, we were able to generate coherent answers for Shared Task 2."
}
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<abstract>Although retrieval augmented generation (RAG) has proven to be an effective approach for creating question-answering systems on a corpus of documents, there is a need to improve the performance of these systems, especially in the regulatory domain where clear and accurate answers are required. This paper outlines the methodology used in our submission to the Regulatory Information Retrieval and Answer Generation (RIRAG) shared task at the Regulatory Natural Language Processing Workshop (RegNLP 2025). The goal is to improve document retrieval (Shared Task 1) and answer generation (Shared Task 2). Our pipeline is constructed as a two-step process for Shared Task 1. In the first step, we utilize a text-embedding-ada-002-based retriever, followed by a RankGPT-based re-ranker. The ranked results of Task 1 are then used to generate responses to user queries in Shared Task 2 through a prompt-based approach using GPT-4o. For Shared Task 1, we achieved a recall rate of 75%, and with the prompts we developed, we were able to generate coherent answers for Shared Task 2.</abstract>
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%0 Conference Proceedings
%T Regulatory Question-Answering using Generative AI
%A Quinn, Devin
%A Pai, Sumit P.
%A Yousfi, Iman
%A Pudota, Nirmala
%A Bhattacharya, Sanmitra
%Y Gokhan, Tuba
%Y Wang, Kexin
%Y Gurevych, Iryna
%Y Briscoe, Ted
%S Proceedings of the 1st Regulatory NLP Workshop (RegNLP 2025)
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F quinn-etal-2025-regulatory
%X Although retrieval augmented generation (RAG) has proven to be an effective approach for creating question-answering systems on a corpus of documents, there is a need to improve the performance of these systems, especially in the regulatory domain where clear and accurate answers are required. This paper outlines the methodology used in our submission to the Regulatory Information Retrieval and Answer Generation (RIRAG) shared task at the Regulatory Natural Language Processing Workshop (RegNLP 2025). The goal is to improve document retrieval (Shared Task 1) and answer generation (Shared Task 2). Our pipeline is constructed as a two-step process for Shared Task 1. In the first step, we utilize a text-embedding-ada-002-based retriever, followed by a RankGPT-based re-ranker. The ranked results of Task 1 are then used to generate responses to user queries in Shared Task 2 through a prompt-based approach using GPT-4o. For Shared Task 1, we achieved a recall rate of 75%, and with the prompts we developed, we were able to generate coherent answers for Shared Task 2.
%U https://aclanthology.org/2025.regnlp-1.16/
%P 102-106
Markdown (Informal)
[Regulatory Question-Answering using Generative AI](https://aclanthology.org/2025.regnlp-1.16/) (Quinn et al., RegNLP 2025)
ACL
- Devin Quinn, Sumit P. Pai, Iman Yousfi, Nirmala Pudota, and Sanmitra Bhattacharya. 2025. Regulatory Question-Answering using Generative AI. In Proceedings of the 1st Regulatory NLP Workshop (RegNLP 2025), pages 102–106, Abu Dhabi, UAE. Association for Computational Linguistics.