--- title: "Mind Your Steps in Biomedical Named Entity Recognition: First" description: "The authors propose FETA (First Extract, Tag Afterwards), a two-call prompt chain for off-the-shelf LLMs that requires no… 1 reference." authors: ["Darya Shlyk", "Stefano Montanelli", "Marco Mesiti", "Lawrence Hunter"] year: 2026 venue: "Proceedings of the 1st Workshop on Linguistic Analysis for Health (HeaLing 2026)" source: "https://aclanthology.org/2026.healing-1.11/" doi: "10.18653/v1/2026.healing-1.11" 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/shlyk-2026-mind-your-steps-biomedical-named/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/shlyk-2026-mind-your-steps-biomedical-named/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [2026](https://md.research.elamur.ai/free/en/papers/2026/page/41/) / Mind Your Steps in Biomedical Named Entity Recognition: First Extract, Tag Afterwards # Mind Your Steps in Biomedical Named Entity Recognition: First Extract, Tag Afterwards **Authors:** Darya Shlyk, Stefano Montanelli, Marco Mesiti and Lawrence Hunter · **Year:** 2026 · **Venue:** [Proceedings of the 1st Workshop on Linguistic Analysis for Health (HeaLing 2026)](https://md.research.elamur.ai/free/en/venues/healing/) ([Workshop on Linguistic Analysis for Health 2026](https://md.research.elamur.ai/free/en/venues/healing/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.healing-1.11/) · [PDF](https://aclanthology.org/2026.healing-1.11.pdf) · [DOI 10.18653/v1/2026.healing-1.11](https://doi.org/10.18653/v1/2026.healing-1.11) · [OpenAlex](https://openalex.org/W7140111923) **Citations:** 0 citations (OpenAlex, 2026-10-14) ## Summary Biomedical named entity recognition is difficult for few-shot prompted LLMs. The domain has vast vocabularies, terms that belong to several classes, and ambiguous local abbreviations, so these models still trail supervised, fine-tuned systems. The authors propose FETA (First Extract, Tag Afterwards), a two-call prompt chain for off-the-shelf LLMs that requires no domain-specific training. In the first call, the model lists candidate mentions. It is steered mainly by expert-written annotation guidelines rather than by demonstrations. In the second call, the model marks every candidate inside the original passage with HTML-style type tags. Candidates assigned a type outside the target set are discarded. This step lets the model re-check its own predictions using only the surrounding context. It needs no knowledge bases or entity linking and handles all candidates in a single pass. Evaluated on several established biomedical benchmarks with commercial and open-source LLMs, FETA reports state-of-the-art results that surpass fine-tuned BioNER models. The work shows that careful prompting can make general LLMs useful where labelled biomedical data is scarce. ## Key points - Ablation, average F1 gains over the base setup: - Type disambiguation: +3.5 (exact match) and +3.8 (relaxed match). - Annotation guidelines: +1.8 and +2.3. - Both combined: +5.3 and +5.9. - Guidelines mainly raise recall, while the tagging step mainly raises precision. - A prompt with guidelines and a single example beats a prompt with five examples and no guidelines. - Comparison with other verification methods (exact-match F1): | Method | BC5CDR | GENIA | |---|---|---| | FETA | 79.2 | 58.0 | | VerifiNER | 61.9 | 55.5 | | GPT-NER | 59.5 | 48.3 | - Verification costs one LLM call per passage (O(1)). VerifiNER and GPT-NER check entities one at a time, so their cost grows linearly (O(n)) with the number of predicted entities. - The moderately sized open model Gemma 2 performs on par with commercial models of similar size and can rival the larger LLaMA 3.3. *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 - [University of Chicago](https://md.research.elamur.ai/free/en/universities/university-of-chicago/) — University, Chicago, United States · 52 works · 50,893 citations - [University of Milan](https://md.research.elamur.ai/free/en/universities/university-of-milan/) — University, Milan, Italy · 7 works · 38,856 citations 📚 Shelves (7): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 18,614 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 17,965 works · [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 17,450 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 17,296 works · [👁️ Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · 3,486 works · [🧬 Biochemistry, Genetics and Molecular Biology](https://md.research.elamur.ai/free/en/shelves/biochemistry-genetics-and-molecular-biology/) · 263 works · [🧬 Molecular Biology](https://md.research.elamur.ai/free/en/shelves/biochemistry-genetics-and-molecular-biology/molecular-biology/) · 260 works 🧭 Topics (1): [🏷️ Named entity recognition](https://md.research.elamur.ai/free/en/topics/named-entity-recognition/) · 214 works ## Cite Cite as: Darya Shlyk, Stefano Montanelli, Marco Mesiti and Lawrence Hunter. 2026. Mind Your Steps in Biomedical Named Entity Recognition: First Extract, Tag Afterwards. Proceedings of the 1st Workshop on Linguistic Analysis for Health (HeaLing 2026). The paper itself, not this page. BibTeX from the library's record of the work. ```bibtex @inproceedings{shlyk2026mind, title = {{Mind Your Steps in Biomedical Named Entity Recognition: First Extract, Tag Afterwards}}, author = {Darya Shlyk, Stefano Montanelli, Marco Mesiti and Lawrence Hunter}, booktitle = {Proceedings of the 1st Workshop on Linguistic Analysis for Health (HeaLing 2026)}, year = {2026}, doi = {10.18653/v1/2026.healing-1.11}, url = {https://doi.org/10.18653/v1/2026.healing-1.11} } ``` - [← previous Mind Your Special Tokens! On the Importance of Dedicated Sequence-End Tokens in Vision-Language Embedding Models](https://md.research.elamur.ai/free/en/papers/musacchio-2026-mind-your-special-tokens-importance/) - [next → Mind's Eye: A Benchmark of Visual Abstraction, Transformation and Composition for Multimodal LLMs](https://md.research.elamur.ai/free/en/papers/sinha-2026-mind-s-eye-benchmark-visual/) - [↑ back to papers of 2026](https://md.research.elamur.ai/free/en/papers/2026/page/41/) ## References 1 references, 1 of them with a card of ours. ↗ leads to the work. 1. Monica Agrawal, Stefan Hegselmann, Hunter Lang, Yoon Kim, and David A. Sontag. 2022. [Large language models are few-shot clinical information extractors](https://md.research.elamur.ai/free/en/papers/agrawal-2022-large-language-models-are-few/). In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022, Abu Dhabi, United Arab Emirates, December 7-… *Updated 1 November 2026*