# LoRE: Logit-Ranked Retriever Ensemble for Enhancing Open-Domain Question Answering Source: [LoRE: Logit-Ranked Retriever Ensemble for Enhancing Open-Domain Question Answering]() Collection index: [llms.txt]() Data updated: 2025-10-27T06:41:00.437Z Authors: Saikrishna Sanniboina, Shiv Trivedi, Sreenidhi Vijayaraghavan Published: 2024-10-13T23:06:08.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()