DynamicER: Resolving Emerging Mentions to Dynamic Entities for RAG (2410.11494v1)

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.

• The paper introduces the DynamicER benchmark to evaluate models on resolving emerging entity mentions in retrieval-augmented generation tasks.
• It proposes a temporal segmented clustering method that adapts to evolving language and outperforms existing baselines in entity linking and QA.
• Experimental results, particularly in sports domain datasets, demonstrate significant improvements in accuracy for low lexical overlap cases.

DynamicER: Resolving Emerging Mentions to Dynamic Entities for RAG

The paper "DynamicER: Resolving Emerging Mentions to Dynamic Entities for RAG" addresses a critical challenge in the evolving linguistic landscape, where new expressions frequently emerge, complicating the retrieval-augmented generation (RAG) process. This can lead to generator hallucinations due to retrieval failures. The authors present the "DynamicER" benchmark and propose a temporal segmented clustering method with continual adaptation, aimed at managing dynamic entity resolution effectively.

Key Contributions

1. DynamicER Benchmark: The paper introduces DynamicER, a benchmark designed to rigorously assess the capability of models in resolving dynamic and emerging mentions of entities. The benchmark comprises two tasks: entity linking and entity-centric question-answering (QA) within the RAG framework.
2. Temporal Segmented Clustering: The proposed method involves clustering emerging mentions across time segments, enhancing the adaptability and accuracy of entity resolution. This approach emphasizes maintaining temporal dynamics, which helps distinguish between evolving entities and their varied mentions.
3. Experimental Validation: Extensive experiments demonstrate that their proposed temporal clustering method outperforms existing baselines in entity linking tasks. The results indicate notable improvements in RAG model performance on QA tasks, particularly for resolving complex and low lexical overlap mentions.

Strong Numerical Results

The authors conducted experiments on large datasets within the sports domain, using advanced LLMs and retrievers. Their temporal segmented clustering method shows consistent superiority over other methods such as SpEL and ArboEL, with significant improvements in cases where mention-to-entity lexical similarity is low. Notably, their method achieves a notable increase in accuracy for low similarity sets, which is often a challenging condition for entity linking.

Implications and Future Directions

The practical implications of this research are significant for the real-world application of RAG frameworks, especially in rapidly updating domains such as news and social media. Theoretically, the introduction of temporal dynamics into clustering methodologies creates a new paradigm for handling evolving language, which may spur further innovations in dynamic knowledge representation and retrieval.

Future research may build upon this work by integrating methods to update and maintain knowledge bases in real-time, addressing retrieval issues more directly. Additionally, exploring more sophisticated clustering methods that account for linguistic nuances beyond temporal dynamics could further improve entity resolution accuracy.

Conclusion

The DynamicER benchmark and the accompanying temporal segmented clustering method mark a substantial step forward in resolving dynamic entity mentions for RAG systems. By effectively managing temporal dynamics and emerging expressions, this approach provides a robust framework that enhances both the adaptability and performance of RAG models. This work opens new avenues for research in dynamic language evolution and entity resolution, promising more reliable and accurate retrieval-augmented generation systems in the future.