LoRE: Logit-Ranked Retriever Ensemble for Enhancing Open-Domain Question Answering

• URL: http://arxiv.org/abs/2410.10042v1
• Date: Sun, 13 Oct 2024 23:06:08 GMT
• Title: LoRE: Logit-Ranked Retriever Ensemble for Enhancing Open-Domain Question Answering
• Authors: Saikrishna Sanniboina, Shiv Trivedi, Sreenidhi Vijayaraghavan,
• Abstract summary: We propose LoRE, a novel approach that improves answer accuracy and relevance by mitigating positional bias. LoRE employs an ensemble of diverse retrievers, such as BM25 and sentence transformers with FAISS indexing. A key innovation is a logit-based answer ranking algorithm that combines the logit scores from a large language model with the retrieval ranks of the passages.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Retrieval-based question answering systems often suffer from positional bias, leading to suboptimal answer generation. We propose LoRE (Logit-Ranked Retriever Ensemble), a novel approach that improves answer accuracy and relevance by mitigating positional bias. LoRE employs an ensemble of diverse retrievers, such as BM25 and sentence transformers with FAISS indexing. A key innovation is a logit-based answer ranking algorithm that combines the logit scores from a large language model (LLM), with the retrieval ranks of the passages. Experimental results on NarrativeQA, SQuAD demonstrate that LoRE significantly outperforms existing retrieval-based methods in terms of exact match and F1 scores. On SQuAD, LoRE achieves 14.5\%, 22.83\%, and 14.95\% improvements over the baselines for ROUGE-L, EM, and F1, respectively. Qualitatively, LoRE generates more relevant and accurate answers, especially for complex queries.

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