Transform Retrieval for Textual Entailment in RAG

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.