LlmLink: Dual LLMs for Dynamic Entity Linking on Long Narratives with Collaborative Memorisation and Prompt Optimisation (2025.coling-main)
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| Challenge: | Existing methods focus on supervised fine-tuning or limited to one-off prediction, which poses a challenge where the context is long. |
| Approach: | They propose a dynamic approach to CoREFerence resolution in chunked long narratives by deploying dual Large Language Models. |
| Outcome: | The proposed model achieves performance gains over existing models and fine-tuning approaches on long narrative datasets, significantly reducing the resources required for inference and training. |
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| Challenge: | Entity linking (EL) focuses on associating ambiguous mentions in text with corresponding entities in a knowledge graph. |
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| Challenge: | Large Language Models (LLMs) are a new approach to coreference resolution, but their performance is not yet fully understood. |
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| Challenge: | Existing approaches to solve entity linking (EL) jointly with coreference resolution (coref) a coreferenced cluster can only be linked to a single entity or NIL (i.e., a nonlinkable entity) |
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Pei Chen, Hongye Jin, Cheng-Che Lee, Rulin Shao, Jingfeng Yang, Mingyu Zhao, Zhaoyu Zhang, Qin Lu, Kaiwen Men, Ning Xie, Huasheng Li, Bing Yin, Han Li, Lingyun Wang
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| Challenge: | Existing Entity Linking methods focus on designing complex multimodal interaction mechanisms and require fine-tuning all model parameters. |
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| Challenge: | Current methods for improving large language models rely on splitting long contexts into fixed-length chunks, compromising accuracy. |
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