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        <title>Context Filtering with Reward Modeling in Question Answering</title>
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            <namePart type="given">Owen</namePart>
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    <abstract>Question Answering (QA) in NLP is the task of finding answers to a query within a relevant context retrieved by a retrieval system. Yet, the mix of relevant and irrelevant information in these contexts can hinder performance enhancements in QA tasks. To address this, we introduce a context filtering approach that removes non-essential details, summarizing crucial content through Reward Modeling. This method emphasizes keeping vital data while omitting the extraneous during summarization model training. We offer a framework for developing efficient QA models by discerning useful information from dataset pairs, bypassing the need for costly human evaluation. Furthermore, we show that our approach can significantly outperform the baseline, as evidenced by a 6.8-fold increase in the EM Per Token (EPT) metric, which we propose as a measure of token efficiency, indicating a notable token-efficiency boost for low-resource settings.</abstract>
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%0 Conference Proceedings
%T Context Filtering with Reward Modeling in Question Answering
%A Kim, Sangryul
%A Thorne, James
%Y Rambow, Owen
%Y Wanner, Leo
%Y Apidianaki, Marianna
%Y Al-Khalifa, Hend
%Y Eugenio, Barbara Di
%Y Schockaert, Steven
%S Proceedings of the 31st International Conference on Computational Linguistics
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi, UAE
%F kim-thorne-2025-context
%X Question Answering (QA) in NLP is the task of finding answers to a query within a relevant context retrieved by a retrieval system. Yet, the mix of relevant and irrelevant information in these contexts can hinder performance enhancements in QA tasks. To address this, we introduce a context filtering approach that removes non-essential details, summarizing crucial content through Reward Modeling. This method emphasizes keeping vital data while omitting the extraneous during summarization model training. We offer a framework for developing efficient QA models by discerning useful information from dataset pairs, bypassing the need for costly human evaluation. Furthermore, we show that our approach can significantly outperform the baseline, as evidenced by a 6.8-fold increase in the EM Per Token (EPT) metric, which we propose as a measure of token efficiency, indicating a notable token-efficiency boost for low-resource settings.
%U https://aclanthology.org/2025.coling-main.732/
%P 11048-11055

Markdown (Informal)

[Context Filtering with Reward Modeling in Question Answering](https://aclanthology.org/2025.coling-main.732/) (Kim & Thorne, COLING 2025)

• Context Filtering with Reward Modeling in Question Answering (Kim & Thorne, COLING 2025)

ACL

• Sangryul Kim and James Thorne. 2025. Context Filtering with Reward Modeling in Question Answering. In Proceedings of the 31st International Conference on Computational Linguistics, pages 11048–11055, Abu Dhabi, UAE. Association for Computational Linguistics.