---
title: "Bridging Context Gaps — paper by Liu et al. (2024)"
description: "The authors propose Long Question Coreference Adaptation (LQCA), a preprocessing pipeline that… 44 references, cited by 8 papers here."
authors: ["Yanming Liu", "Xinyue Peng", "Jiannan Cao", "Yanxin Shen", "Tianyu Du", "Sheng Cheng", "Xun Wang", "Jianwei Yin", "Xuhong Zhang"]
year: 2024
venue: "arXiv:2410.01671"
source: "https://arxiv.org/abs/2410.01671"
doi: "10.48550/arxiv.2410.01671"
arxiv: "2410.01671"
licence: "CC BY-SA 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by-sa/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/liu-2024-bridging-context-gaps-leveraging-coreference/"
---
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# Bridging Context Gaps: Leveraging Coreference Resolution for Long Contextual Understanding

**Authors:** [Yanming Liu](https://md.research.elamur.ai/free/en/authors/yanming-liu/), [Xinyue Peng](https://md.research.elamur.ai/free/en/authors/xinyue-peng/), [Jiannan Cao](https://md.research.elamur.ai/free/en/authors/jiannan-cao/), Yanxin Shen, [Tianyu Du](https://md.research.elamur.ai/free/en/authors/tianyu-du/), Sheng Cheng, [Xun Wang](https://md.research.elamur.ai/free/en/authors/xun-wang/), [Jianwei Yin](https://md.research.elamur.ai/free/en/authors/jianwei-yin/) and [Xuhong Zhang](https://md.research.elamur.ai/free/en/authors/xuhong-zhang/) · **Year:** 2024 · **Venue:** arXiv:2410.01671 · **Licence:** [CC BY-SA 4.0 (arXiv)](http://creativecommons.org/licenses/by-sa/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2410.01671) · [PDF](https://arxiv.org/pdf/2410.01671) · [DOI 10.48550/arxiv.2410.01671](https://doi.org/10.48550/arxiv.2410.01671) · [arXiv 2410.01671](https://arxiv.org/abs/2410.01671) · [OpenAlex](https://openalex.org/W4403852011)

**Citations:** 2 citations (OpenAlex, 2026-10-14) · 8 in our library, of the 149,439 papers whose bibliographies we read

## Summary
LLMs still struggle with long inputs. When a document uses many pronouns, modifiers and varied noun phrases for the same entities, models lose track of the referents, especially in mid-document passages. The authors propose Long Question Coreference Adaptation (LQCA), a preprocessing pipeline that rewrites the context before an LLM answers questions about it.

The text is first split into sentence-aligned sub-documents short enough for the Maverick coreference model (maverick-mes-ontonotes, built on DeBERTa-v3). Coreference decisions within each chunk become pairwise mention scores. These scores are linked across chunks by multiplying them through shared mentions. A Dijkstra-like "longest dot product path" search and a threshold k then produce a mention graph whose components form document-wide clusters. Each cluster receives a representative mention, which replaces the other references.

Across five LLMs and nine long-context datasets, the rewritten inputs improved answers, with a 3.61% gain on GPT-4. The work suggests that cleaner input text, not just bigger context windows, matters for long-context reasoning.

## Key points
- **Four LQCA steps:** sub-document coreference resolution, mention distance computation, choice of a representative mention per cluster, and question answering on the mention-replaced text.
- **Coreference backbone:** the Maverick model `sapienzanlp/maverick-mes-ontonotes` (DeBERTa-v3) is run in batches on sliding-window chunks capped at length L. Chunks begin at sentence boundaries, and overflowing sentences are truncated.
- **Cross-chunk merging:**
  - Mention distance is the share of co-reference judgments among all judgments for a pair.
  - Indirect links take the maximum product of distances through a third mention.
  - Pairs scoring above threshold k become graph edges, and the resulting components form clusters.
  - Ambiguous mentions are kept as separate clusters and are not replaced.
- **Evaluation:**
  - The method was tested on five LLMs of different sizes and nine long-context datasets.
  - The introduction reports a 3.61% improvement on GPT-4, and the abstract highlights gains on OpenAI-o1-mini and GPT-4o.
  - Tasks include long-text QA and long-text classification.
- **Robustness and resources:**
  - A perturbation test randomly shifts 5% of mentions by up to ±10, and performance drops (Table 2), underlining the role of correct coreference.
  - Code is available at github.com/OceannTwT/LQCA.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Massachusetts Institute of Technology](https://md.research.elamur.ai/free/en/universities/massachusetts-institute-of-technology/) — University, Cambridge, United States · 243 works · 76,506 citations
- [Zhejiang University](https://md.research.elamur.ai/free/en/universities/zhejiang-university/) — University, Hangzhou, China · 224 works · 13,587 citations
- [Southeast University](https://md.research.elamur.ai/free/en/universities/southeast-university-cn/) — University, Nanjing, China · 32 works · 453 citations

Also on the byline: [Ocean Institute](https://ror.org/03574zr06) · [Deep Ocean Technology](https://ror.org/02vkr9a39).

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🧭 Topics (1): [🔗 Coreference resolution](https://md.research.elamur.ai/free/en/topics/coreference-resolution/) · 575 works

## Cite

Cite as: Yanming Liu, Xinyue Peng, Jiannan Cao, Yanxin Shen, Tianyu Du, Sheng Cheng, Xun Wang, Jianwei Yin and Xuhong Zhang. 2024. Bridging Context Gaps: Leveraging Coreference Resolution for Long Contextual Understanding. arXiv:2410.01671.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{liu2024bridging,
  title = {{Bridging Context Gaps: Leveraging Coreference Resolution for Long Contextual Understanding}},
  author = {Yanming Liu, Xinyue Peng, Jiannan Cao, Yanxin Shen, Tianyu Du, Sheng Cheng, Xun Wang, Jianwei Yin and Xuhong Zhang},
  year = {2024},
  doi = {10.48550/arxiv.2410.01671},
  eprint = {2410.01671},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2410.01671}
}
```

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*Updated 31 October 2026*