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        <title>DynamicER: Resolving Emerging Mentions to Dynamic Entities for RAG</title>
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    <abstract>In the rapidly evolving landscape of language, resolving new linguistic expressions in continuously updating knowledge bases remains a formidable challenge. This challenge becomes critical in retrieval-augmented generation (RAG) with knowledge bases, as emerging expressions hinder the retrieval of relevant documents, leading to generator hallucinations. To address this issue, we introduce a novel task aimed at resolving emerging mentions to dynamic entities and present DynamicER benchmark. Our benchmark includes dynamic entity mention resolution and entity-centric knowledge-intensive QA task, evaluating entity linking and RAG model’s adaptability to new expressions, respectively. We discovered that current entity linking models struggle to link these new expressions to entities. Therefore, we propose a temporal segmented clustering method with continual adaptation, effectively managing the temporal dynamics of evolving entities and emerging mentions. Extensive experiments demonstrate that our method outperforms existing baselines, enhancing RAG model performance on QA task with resolved mentions.</abstract>
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%0 Conference Proceedings
%T DynamicER: Resolving Emerging Mentions to Dynamic Entities for RAG
%A Kim, Jinyoung
%A Ko, Dayoon
%A Kim, Gunhee
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F kim-etal-2024-dynamicer
%X In the rapidly evolving landscape of language, resolving new linguistic expressions in continuously updating knowledge bases remains a formidable challenge. This challenge becomes critical in retrieval-augmented generation (RAG) with knowledge bases, as emerging expressions hinder the retrieval of relevant documents, leading to generator hallucinations. To address this issue, we introduce a novel task aimed at resolving emerging mentions to dynamic entities and present DynamicER benchmark. Our benchmark includes dynamic entity mention resolution and entity-centric knowledge-intensive QA task, evaluating entity linking and RAG model’s adaptability to new expressions, respectively. We discovered that current entity linking models struggle to link these new expressions to entities. Therefore, we propose a temporal segmented clustering method with continual adaptation, effectively managing the temporal dynamics of evolving entities and emerging mentions. Extensive experiments demonstrate that our method outperforms existing baselines, enhancing RAG model performance on QA task with resolved mentions.
%U https://aclanthology.org/2024.emnlp-main.762
%P 13752-13770

Markdown (Informal)

[DynamicER: Resolving Emerging Mentions to Dynamic Entities for RAG](https://aclanthology.org/2024.emnlp-main.762) (Kim et al., EMNLP 2024)

• DynamicER: Resolving Emerging Mentions to Dynamic Entities for RAG (Kim et al., EMNLP 2024)

ACL

• Jinyoung Kim, Dayoon Ko, and Gunhee Kim. 2024. DynamicER: Resolving Emerging Mentions to Dynamic Entities for RAG. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 13752–13770, Miami, Florida, USA. Association for Computational Linguistics.