@inproceedings{guo-liang-2025-transform,
title = "Transform Retrieval for Textual Entailment in {RAG}",
author = "Liang, Xin and
Guo, Quan",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.naacl-short.50/",
doi = "10.18653/v1/2025.naacl-short.50",
pages = "595--599",
ISBN = "979-8-89176-190-2",
abstract = "In this paper, we introduce Transform Retrieval, a novel approach aimed at improving Textual Entailment Retrieval within the framework of Retrieval-Augmented Generation (RAG). While RAG has shown promise in enhancing Large Language Models by retrieving relevant documents to extract specific knowledge or mitigate hallucination, current retrieval methods often prioritize relevance without ensuring the retrieved documents semantically support answering the queries. Transform Retrieval addresses this gap by transforming query embeddings to better align with semantic entailment without re-encoding the document corpus. We achieve this by using a transform model and employing a contrastive learning strategy to optimize the alignment between transformed query embeddings and document embeddings for better entailment.We evaluated the framework using BERT as frozen pre-trained encoder and compared it with a fully fine-tuned skyline model. Experimental results show that Transform Retrieval with simple MLP consistently approaches the skyline across multiple datasets, demonstrating the method{'}s effectiveness. The high performance on HotpotQA highlights its strength in many-to-many retrieval scenarios."
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%0 Conference Proceedings
%T Transform Retrieval for Textual Entailment in RAG
%A Liang, Xin
%A Guo, Quan
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-190-2
%F guo-liang-2025-transform
%X In this paper, we introduce Transform Retrieval, a novel approach aimed at improving Textual Entailment Retrieval within the framework of Retrieval-Augmented Generation (RAG). While RAG has shown promise in enhancing Large Language Models by retrieving relevant documents to extract specific knowledge or mitigate hallucination, current retrieval methods often prioritize relevance without ensuring the retrieved documents semantically support answering the queries. Transform Retrieval addresses this gap by transforming query embeddings to better align with semantic entailment without re-encoding the document corpus. We achieve this by using a transform model and employing a contrastive learning strategy to optimize the alignment between transformed query embeddings and document embeddings for better entailment.We evaluated the framework using BERT as frozen pre-trained encoder and compared it with a fully fine-tuned skyline model. Experimental results show that Transform Retrieval with simple MLP consistently approaches the skyline across multiple datasets, demonstrating the method’s effectiveness. The high performance on HotpotQA highlights its strength in many-to-many retrieval scenarios.
%R 10.18653/v1/2025.naacl-short.50
%U https://aclanthology.org/2025.naacl-short.50/
%U https://doi.org/10.18653/v1/2025.naacl-short.50
%P 595-599
Markdown (Informal)
[Transform Retrieval for Textual Entailment in RAG](https://aclanthology.org/2025.naacl-short.50/) (Liang & Guo, NAACL 2025)
ACL
- Xin Liang and Quan Guo. 2025. Transform Retrieval for Textual Entailment in RAG. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers), pages 595–599, Albuquerque, New Mexico. Association for Computational Linguistics.