English(EN) CaRL-EM: Cost-Aware Reinforcement Learning for Entity Matching with LLMs

新的强化学习方法优化LLM实体匹配的成本和质量

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-02 04:00

研究人员开发了CaRL-EM，一种使用大型语言模型（LLMs）进行实体匹配的新型强化学习方法。该方法通过根据任务复杂度自适应地选择算子和模型容量，来优化匹配质量和推理成本之间的权衡。CaRL-EM在各种数据集和领域中展示了强大的零样本迁移能力，在实现更好的质量-成本平衡方面优于现有的基于LLM的基线和手动流程。 AI

影响优化实体匹配任务的LLM推理成本，可能提高数据处理和集成的效率。

排序理由学术论文，详细介绍了一种新的基于LLM的实体匹配方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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本文如何被排名

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报道来源 [1]

arXiv cs.CL TIER_1 English(EN) · Chaohui Guo, Michel Klein, Zhisheng Huang · 2026-09-02 04:00

CaRL-EM：LLM驱动的成本感知实体匹配强化学习

arXiv:2609.01195v1 Announce Type: new Abstract: Entity matching (EM) requires fine-grained contextual understanding and domain knowledge. Recent work shows that large language models (LLMs) can serve as strong matchers across domains, but most methods either make independent pair…

本文如何被排名

报道来源 [1]

CaRL-EM：LLM驱动的成本感知实体匹配强化学习

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