Can we repurpose multiple-choice question-answering models to rerank retrieved documents?

• URL: http://arxiv.org/abs/2504.06276v1
• Date: Thu, 06 Mar 2025 17:53:24 GMT
• Title: Can we repurpose multiple-choice question-answering models to rerank retrieved documents?
• Authors: Jasper Kyle Catapang,
• Abstract summary: R* is a proof-of-concept model that harmonizes multiple-choice question-answering (MCQA) models for document reranking.<n>Through experimental validation, R* proves to improve retrieval accuracy and contribute to the field's advancement.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Yes, repurposing multiple-choice question-answering (MCQA) models for document reranking is both feasible and valuable. This preliminary work is founded on mathematical parallels between MCQA decision-making and cross-encoder semantic relevance assessments, leading to the development of R*, a proof-of-concept model that harmonizes these approaches. Designed to assess document relevance with depth and precision, R* showcases how MCQA's principles can improve reranking in information retrieval (IR) and retrieval-augmented generation (RAG) systems -- ultimately enhancing search and dialogue in AI-powered systems. Through experimental validation, R* proves to improve retrieval accuracy and contribute to the field's advancement by demonstrating a practical prototype of MCQA for reranking by keeping it lightweight.

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