# LLMBridge: An LLM Pipeline for End-to-end Referential Bridging Resolution in English

arXiv: https://arxiv.org/abs/2605.29048 · HTML: https://mlsanity.com/paper/2605.29048/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 47 references that are in the map.
- First posted on arXiv 27 May 2026.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://mlsanity.com/paper/1907.10529/) (2019)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Bridging Anaphora Resolution as Question Answering](https://mlsanity.com/paper/2004.07898/) (2020)
- [Multi-task Learning Based Neural Bridging Reference Resolution](https://mlsanity.com/paper/2003.03666/) (2020)
- [Subjectivity in the Annotation of Bridging Anaphora](https://mlsanity.com/paper/2506.07297/) (2025)
- [DiscoTrack: A Multilingual LLM Benchmark for Discourse Tracking](https://mlsanity.com/paper/2510.17013/) (2025)
- [GUMBridge: a Corpus for Varieties of Bridging Anaphora](https://mlsanity.com/paper/2512.07134/) (2025)

## Abstract (arXiv)

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.