---
title: "ToMMeR - Efficient Entity Mention Detection from Large"
description: "They introduce ToMMeR, a probe with fewer than 300K parameters attached to an early layer of a… 60 references, cited by 2 papers here."
authors: ["Victor Morand", "Nadi Tomeh", "Josiane Mothe", "Benjamin Piwowarski"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.1268/"
doi: "10.18653/v1/2026.acl-long.1268"
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/morand-2026-tommer-efficient-entity-mention-detection/"
---
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# ToMMeR - Efficient Entity Mention Detection from Large Language Models

**Authors:** Victor Morand, [Nadi Tomeh](https://md.research.elamur.ai/free/en/authors/nadi-tomeh/), [Josiane Mothe](https://md.research.elamur.ai/free/en/authors/josiane-mothe/) and [Benjamin Piwowarski](https://md.research.elamur.ai/free/en/authors/benjamin-piwowarski/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.1268/) · [PDF](https://aclanthology.org/2026.acl-long.1268.pdf) · [DOI 10.18653/v1/2026.acl-long.1268](https://doi.org/10.18653/v1/2026.acl-long.1268) · [OpenAlex](https://openalex.org/W4416246826)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · [2](https://md.research.elamur.ai/free/en/papers/morand-2026-tommer-efficient-entity-mention-detection/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Entity mention detection, the task of finding which text spans name entities or concepts, is a known weak point of NER systems and is usually merged with entity typing. The authors argue that span boundaries do not depend on any schema and may already be encoded inside pretrained LLMs. They introduce ToMMeR, a probe with fewer than 300K parameters attached to an early layer of a frozen LLM. It scores candidate spans by combining cosine-based token-to-token matching scores, computed in a low-rank query-key space, with per-token values. A small logistic model turns these into a span probability. ToMMeR is trained only on span boundaries from Pile-NER, using balanced binary cross-entropy. A second training round adds mentions the first model found but the data missed. Across 13 NER benchmarks it reaches high zero-shot recall and estimated precision near 90%. It needs only a partial forward pass, with no prompting and no text generation. Backbones of very different sizes produce similar spans. This suggests mention detection emerges from language modeling itself, which makes ToMMeR a cheap, schema-agnostic first stage for IE and RAG pipelines.

## Key points
- **Zero-shot results:** ToMMeR reaches 93% average recall on 13 NER benchmarks and also transfers well to other Latin-script languages.
- **Precision estimate:** Precision of about 90% was estimated with a gpt-4.1-mini judge. The judge agreed with humans on 87.7% of 1,800 annotated mentions, and PPI analysis places true precision above 89%.
- **Cross-model consistency:** Backbones from 14M to 15B parameters, both autoregressive and encoder-only, converge on similar spans (DICE >0.75).
- **Efficiency and full NER:** ToMMeR runs 42× faster than prompting-based extraction. Adding a span-typing head gives 80–87% F1 on standard benchmarks, including 80.4% on OntoNotes with Llama-3.2-1B, comparable to EMBER.
- **Data and limitations:** Training uses Pile-NER (45,889 samples from The Pile, annotated by GPT-3.5). Spans are restricted to contiguous mentions of at most 25 tokens. Recall drops to 42% on ACE 2005 because its annotation conventions differ, for example by including determiners in spans. Code is available at github.com/VictorMorand/llm2ner.

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

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- [Institut Systèmes Intelligents et de Robotique](https://md.research.elamur.ai/free/en/labs/institut-systemes-intelligents-et-de-robotique/) — Research institute, Paris, France · 8 works · 206 citations
- [Université Toulouse - Jean Jaurès](https://md.research.elamur.ai/free/en/universities/universite-toulouse-jean-jaures/) — University, Toulouse, France · 2 works · 3 citations

Also on the byline: [Laboratoire Cognition, Langues, Langage, Ergonomie](https://ror.org/029cj5595).

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## Cite

Cite as: Victor Morand, Nadi Tomeh, Josiane Mothe and Benjamin Piwowarski. 2026. ToMMeR - Efficient Entity Mention Detection from Large Language Models. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

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

```bibtex
@inproceedings{morand2026tommer,
  title = {{ToMMeR - Efficient Entity Mention Detection from Large Language Models}},
  author = {Victor Morand, Nadi Tomeh, Josiane Mothe and Benjamin Piwowarski},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1268},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1268}
}
```

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*Updated 1 November 2026*