Document detail
ID
oai:arXiv.org:2406.05733
Topic
Computer Science - Computation and...Author
Khamnuansin, Danupat Chalothorn, Tawunrat Chuangsuwanich, EkapolCategory
Computer Science
Year
2024
listing date
11/27/2024
Keywords
systems irAbstract
Large Language Models (LLMs) often struggle with hallucinations and outdated information.
To address this, Information Retrieval (IR) systems can be employed to augment LLMs with up-to-date knowledge.
However, existing IR techniques contain deficiencies, posing a performance bottleneck.
Given the extensive array of IR systems, combining diverse approaches presents a viable strategy.
Nevertheless, prior attempts have yielded restricted efficacy.
In this work, we propose an approach that leverages learning-to-rank techniques to combine heterogeneous IR systems.
We demonstrate the method on two Retrieval Question Answering (ReQA) tasks.
Our empirical findings exhibit a significant performance enhancement, outperforming previous approaches and achieving state-of-the-art results on ReQA SQuAD.
;Comment: To be published in Findings of ACL 2024
Khamnuansin, Danupat,Chalothorn, Tawunrat,Chuangsuwanich, Ekapol, 2024, MrRank: Improving Question Answering Retrieval System through Multi-Result Ranking Model
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