Inter-Passage Verification for Multi-evidence Multi-answer QA

Multi-answer question answering (QA), where questions can have many valid answers, presents a significant challenge for existing retrieval-augmented generation-based QA systems, as these systems struggle to retrieve and then synthesize a large number of evidence passages.To tackle these challenges, we propose a new multi-answer QA framework -Retrievalaugmented Independent Reading with Interpassage Verification (RI 2 VER).Our framework retrieves a large set of passages and processes each passage individually to generate an initial high-recall but noisy answer set.Then we propose a new inter-passage verification pipeline that validates every candidate answer through (1) Verification Question Generation, (2) Gathering Additional Evidence, and (3) Verification with inter-passage synthesis.Evaluations on the QAMPARI and RoMQA datasets demonstrate that our framework significantly outperforms existing baselines across various model sizes, achieving an average F1 score improvement of 11.17%.Further analysis validates that our inter-passage verification pipeline enables our framework to be particularly beneficial for questions requiring multievidence synthesis. 1

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Publication Details

Published
2025-01-01
DOI
https://doi.org/10.18653/v1/2025.findings-acl.354
Primary Topic
Topic Modeling
Type
article
Field-Weighted Citation Impact
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article

Inter-Passage Verification for Multi-evidence Multi-answer QA

Bingsen Chen, Shenji Wan, Xi Ye, Chen Zhao
Topic Modeling
article

Inter-Passage Verification for Multi-evidence Multi-answer QA

Bingsen Chen, Shenji Wan, Xi Ye, Chen Zhao
article en

Abstract

Multi-answer question answering (QA), where questions can have many valid answers, presents a significant challenge for existing retrieval-augmented generation-based QA systems, as these systems struggle to retrieve and then synthesize a large number of evidence passages.To tackle these challenges, we propose a new multi-answer QA framework -Retrievalaugmented Independent Reading with Interpassage Verification (RI 2 VER).Our framework retrieves a large set of passages and processes each passage individually to generate an initial high-recall but noisy answer set.Then we propose a new inter-passage verification pipeline that validates every candidate answer through (1) Verification Question Generation, (2) Gathering Additional Evidence, and (3) Verification with inter-passage synthesis.Evaluations on the QAMPARI and RoMQA datasets demonstrate that our framework significantly outperforms existing baselines across various model sizes, achieving an average F1 score improvement of 11.17%.Further analysis validates that our inter-passage verification pipeline enables our framework to be particularly beneficial for questions requiring multievidence synthesis. 1

New York University Shanghai
Openalex Percentile: Top 90%
Topic Modeling
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