@inproceedings{faisal-etal-2025-nust,
title = "{NUST} Alpha at {RIRAG} 2025: Fusion {RAG} for Bridging Lexical and Semantic Retrieval and Question Answering",
author = "Faisal, Muhammad Rouhan and
Abdullah, Muhammad and
Shah, Faizyaab Ali and
Riaz, Shalina and
Ameer, Huma and
Latif, Seemab and
Fatima, Mehwish",
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-1.12/",
pages = "79--84",
abstract = "NUST Alpha participates in the Regulatory Information Retrieval and Answer Generation (RIRAG) shared task. We propose FusionRAG that combines OpenAI embeddings, BM25, FAISS, and Rank-Fusion to improve information retrieval and answer generation. We also explores multiple variants of our model to assess the impact of each component in overall performance. FusionRAG strength comes from our rank fusion and filter strategy. Rank fusion integrates semantic and lexical relevance scores to optimize retrieval accuracy and result diversity, and Filter mechanism remove irrelevant passages before answer generation. Our experiments demonstrate that FusionRAG offers a robust and scalable solution for automating the analysis of regulatory documents, improving compliance efficiency, and mitigating associated risks. We further conduct an error analysis to explore the limitations of our model`s performance."
}
<?xml version="1.0" encoding="UTF-8"?>
<modsCollection xmlns="http://www.loc.gov/mods/v3">
<mods ID="faisal-etal-2025-nust">
<titleInfo>
<title>NUST Alpha at RIRAG 2025: Fusion RAG for Bridging Lexical and Semantic Retrieval and Question Answering</title>
</titleInfo>
<name type="personal">
<namePart type="given">Muhammad</namePart>
<namePart type="given">Rouhan</namePart>
<namePart type="family">Faisal</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Muhammad</namePart>
<namePart type="family">Abdullah</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Faizyaab</namePart>
<namePart type="given">Ali</namePart>
<namePart type="family">Shah</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Shalina</namePart>
<namePart type="family">Riaz</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Huma</namePart>
<namePart type="family">Ameer</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Seemab</namePart>
<namePart type="family">Latif</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Mehwish</namePart>
<namePart type="family">Fatima</namePart>
<role>
<roleTerm authority="marcrelator" type="text">author</roleTerm>
</role>
</name>
<originInfo>
<dateIssued>2025-01</dateIssued>
</originInfo>
<typeOfResource>text</typeOfResource>
<relatedItem type="host">
<titleInfo>
<title>Proceedings of the 1st Regulatory NLP Workshop (RegNLP 2025)</title>
</titleInfo>
<name type="personal">
<namePart type="given">Tuba</namePart>
<namePart type="family">Gokhan</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Kexin</namePart>
<namePart type="family">Wang</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Iryna</namePart>
<namePart type="family">Gurevych</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<name type="personal">
<namePart type="given">Ted</namePart>
<namePart type="family">Briscoe</namePart>
<role>
<roleTerm authority="marcrelator" type="text">editor</roleTerm>
</role>
</name>
<originInfo>
<publisher>Association for Computational Linguistics</publisher>
<place>
<placeTerm type="text">Abu Dhabi, UAE</placeTerm>
</place>
</originInfo>
<genre authority="marcgt">conference publication</genre>
</relatedItem>
<abstract>NUST Alpha participates in the Regulatory Information Retrieval and Answer Generation (RIRAG) shared task. We propose FusionRAG that combines OpenAI embeddings, BM25, FAISS, and Rank-Fusion to improve information retrieval and answer generation. We also explores multiple variants of our model to assess the impact of each component in overall performance. FusionRAG strength comes from our rank fusion and filter strategy. Rank fusion integrates semantic and lexical relevance scores to optimize retrieval accuracy and result diversity, and Filter mechanism remove irrelevant passages before answer generation. Our experiments demonstrate that FusionRAG offers a robust and scalable solution for automating the analysis of regulatory documents, improving compliance efficiency, and mitigating associated risks. We further conduct an error analysis to explore the limitations of our model‘s performance.</abstract>
<identifier type="citekey">faisal-etal-2025-nust</identifier>
<location>
<url>https://aclanthology.org/2025.regnlp-1.12/</url>
</location>
<part>
<date>2025-01</date>
<extent unit="page">
<start>79</start>
<end>84</end>
</extent>
</part>
</mods>
</modsCollection>
%0 Conference Proceedings
%T NUST Alpha at RIRAG 2025: Fusion RAG for Bridging Lexical and Semantic Retrieval and Question Answering
%A Faisal, Muhammad Rouhan
%A Abdullah, Muhammad
%A Shah, Faizyaab Ali
%A Riaz, Shalina
%A Ameer, Huma
%A Latif, Seemab
%A Fatima, Mehwish
%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 faisal-etal-2025-nust
%X NUST Alpha participates in the Regulatory Information Retrieval and Answer Generation (RIRAG) shared task. We propose FusionRAG that combines OpenAI embeddings, BM25, FAISS, and Rank-Fusion to improve information retrieval and answer generation. We also explores multiple variants of our model to assess the impact of each component in overall performance. FusionRAG strength comes from our rank fusion and filter strategy. Rank fusion integrates semantic and lexical relevance scores to optimize retrieval accuracy and result diversity, and Filter mechanism remove irrelevant passages before answer generation. Our experiments demonstrate that FusionRAG offers a robust and scalable solution for automating the analysis of regulatory documents, improving compliance efficiency, and mitigating associated risks. We further conduct an error analysis to explore the limitations of our model‘s performance.
%U https://aclanthology.org/2025.regnlp-1.12/
%P 79-84
Markdown (Informal)
[NUST Alpha at RIRAG 2025: Fusion RAG for Bridging Lexical and Semantic Retrieval and Question Answering](https://aclanthology.org/2025.regnlp-1.12/) (Faisal et al., RegNLP 2025)
ACL