用大模型解决英文指代衔接问题，端到端效果超越现有方法
LLMBridge: An LLM Pipeline for End-to-end Referential Bridging Resolution in English
- 结合启发式预/后处理与大模型推理能力，构建端到端衔接解析流程
- 在3个数据集上均超越此前最优系统，端到端设置下表现最佳
- 分析了大模型仍难识别的衔接类型，适合语言理解与生成研究者参考
本文提出LLMBridge，一种基于大模型的英文指代衔接解析端到端系统。该系统融合启发式预/后处理与大模型的自然语言推理能力，用于解决英文指代衔接问题。我们在三个常用评估数据集ISNotes、BASHI和GUMBridge上进行了测试，结果表明：在挑战性的端到端评估设置及基础衔接解析设置（给定黄金衔接代词）下，LLMBridge在所有三个数据集上的表现均优于先前最先进系统。我们还对系统性能进行了详细错误分析，探讨了当前大模型仍难以识别的衔接类型。论文同时发布了LLMBridge的代码。
原文摘要 · Abstract (English)
In this paper, we introduce LLMBridge, a new LLM based system for the task of end-to-end referential bridging resolution in English. Our bridging resolution pipeline combines heuristic pre/post-processing with the natural language inference ability that comes from LLMs. We evaluate our bridging resolution pipeline on three datasets which have been used for referential bridging resolution evaluation in English: ISNotes, BASHI, and GUMBridge. Comparison to previous bridging resolution systems shows that the performance of LLMBridge surpasses previous state-of-the-art (SoTA) systems for all 3 datasets in the challenging End-to-end Evaluation Setting, as well as the Basic Bridging Resolution Evaluation Setting (gold bridging anaphor given). We also conduct a thorough error analysis of the LLMBridge performance, examining what varieties of bridging remain difficult for LLM based systems to identify. With this paper, we release the code for the LLMBridge pipeline.
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