---
title: "[Lions: 1] and [Tigers: 2] and [Bears: 3], Oh — Hicke 2024"
description: "Coreference resolution underpins many computational studies of fiction."
authors: ["Rebecca Hicke", "David Mimno"]
year: 2024
venue: "Proceedings of the 8th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2024)"
source: "https://aclanthology.org/2024.latechclfl-1.27/"
doi: "10.18653/v1/2024.latechclfl-1.27"
arxiv: "2401.17922"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/hicke-2024-lions-1-tigers-2-bears/"
---
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# \[Lions: 1\] and \[Tigers: 2\] and \[Bears: 3\], Oh My! Literary Coreference Annotation with LLMs

**Authors:** Rebecca Hicke, [David Mimno](https://md.research.elamur.ai/free/en/authors/david-mimno/) · **Year:** 2024 · **Venue:** Proceedings of the 8th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2024) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2024.latechclfl-1.27/) · [PDF](https://aclanthology.org/2024.latechclfl-1.27.pdf) · [DOI 10.18653/v1/2024.latechclfl-1.27](https://doi.org/10.18653/v1/2024.latechclfl-1.27) · [arXiv 2401.17922](https://arxiv.org/abs/2401.17922) · [OpenAlex](https://openalex.org/W7126377092)

**Citations:** 1 citations (openalex-snapshot · 2026-10-14) · [6](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Coreference resolution underpins many computational studies of fiction. Literary prose is hard for standard systems, though: it needs subtle inference, and the task requires structured output that has usually meant purpose-built software. Hicke and Mimno treat coreference as sequence-to-sequence generation. A model receives a plain sentence and rewrites it with each mention wrapped in brackets and tagged with an integer cluster ID.

They built sentence-level training and test sets from the LitBank corpus of pre-1923 English novels. They then fine-tuned several sizes of T5, mT5 and the decoder-only Pythia family, keeping only models trainable on a single GPU. These were compared with spaCy-based tools (fastcoref, neuralcoref, coreferee).

The fine-tuned t5-3b clearly outperformed every baseline and almost never changed the original wording. On novels outside its training data, it behaved roughly on par with BookNLP. Pythia models failed entirely.

The study suggests that ordinary encoder-decoder models, fine-tuned with a simple and adaptable workflow, can produce rich structured annotation of literary text. This may extend to harder tasks such as character relationships.

## Key points
- **Data:** 92 LitBank novels, each with at least 50 annotated sentences, split into 3,480 training, 174 validation and 4,560 test sentences. Five novels were held out completely and placed in the test set.
- **Main result:** t5-3b reached 91.03 entity F1 and 80.16 coreference F1, versus 50.86 / 40.46 for fastcoref (LingMess), the strongest baseline. It also had an average edit distance of 0.1 from the input and an exact output match on 70.72% of sentences. Its MUC F1 was 89.19.
- **Comparing model families:** mT5-large did reasonably well (81.90 entity F1) and could tag some entities in German, Spanish and French despite English-only fine-tuning. All Pythia models (70m–1.4b) scored 0.0 and tended to append hallucinated text to the copied input.
- **BookNLP comparison:** On 100 sentences from *Orlando* and *The Well of Loneliness*, t5-3b failed to reproduce the input in only two cases, and overall quality looked comparable to BookNLP. BookNLP is trained on LitBank, so it was left out of the main results. The authors still report that t5-3b's CoNLL average exceeded BookNLP's by 9.5%.
- **Resource:** The fine-tuned model is released on Hugging Face as `rmmhicke/t5-literary-coreference`, together with code for training new models.

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

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/en/shelves/linguistics/)

Topics: [Coreference resolution](https://md.research.elamur.ai/free/en/topics/coreference-resolution/)

## Cited by

Cited by

6 papers of our library cite it (of the 140673 whose bibliographies we read); 2 of them have a page here, 4 not yet.

- [Assessing the Capabilities of Large Language Models in Coreference: An Evaluation](https://md.research.elamur.ai/free/en/papers/gan-2024-assessing-capabilities-large-language-models/)
- [Findings of the Fourth Shared Task on Multilingual Coreference Resolution: Can LLMs Dethrone Traditional Approaches?](https://md.research.elamur.ai/free/en/papers/novak-2025-findings-fourth-shared-task-multilingual/)

## Cite

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

```bibtex
@inproceedings{hicke2024lions,
  title = {{[Lions: 1] and [Tigers: 2] and [Bears: 3], Oh My! Literary Coreference Annotation with LLMs}},
  author = {Hicke, Rebecca and Mimno, David},
  booktitle = {Proceedings of the 8th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2024)},
  year = {2024},
  doi = {10.18653/v1/2024.latechclfl-1.27},
  eprint = {2401.17922},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.18653/v1/2024.latechclfl-1.27}
}
```

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## References

29 references, 17 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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