{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/effectiveness-of-cross-linguistic-extraction","title":"Effectiveness of Cross-linguistic Extraction of Genetic Information using Generative Large Language Models","arxiv_id":null,"date":"2024-09-24","proceeding":"Proceedings of the Iberian Languages Evaluation Forum (IberLEF) co-located with the Conference of the Spanish Society for Natural Language Processing (SEPLN) 2024 9","authors":["Milindi Kodikara","Karin Verspoor"],"abstract":"This paper presents the RMIT University system (RMIT-READ-BioMed) developed for the GenoVarDis shared task at IberLEF 2024, focusing on the task of Named Entity Recognition (NER) of genes, genetic variants, and associated diseases from Spanish-language scientific literature texts. The approach involves exploration of a general generative Large Language Model (LLM), GPT-3.5, for NER. We explore the impact of providing English-language instructions with the Spanish-language target text (crosslinguistic setting) as compared to a within-language setting where the instruction language matches the language of the text. We further experiment with a range of instruction strategies, including zero-shot and few-shot prompting under these two settings. Results indicate that the optimal results could be obtained with Englishlanguage instructions under the few-shot learning paradigm, resulting in an F1-score of 0.5. While this approach does not match the top results achieved for the shared task, our experiments provide insight into limitations associated with simple prompting of LLMs in languages other than English.","url_abs":"https://scholar.google.com.au/citations?view_op=view_citation&hl=en&user=hKfyvDQAAAAJ&citation_for_view=hKfyvDQAAAAJ:d1gkVwhDpl0C","url_pdf":"https://ceur-ws.org/Vol-3756/GenoVarDis2024_paper4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"effectiveness-of-cross-linguistic-extraction","repo_url":"https://github.com/Milindi-Kodikara/RMIT-READ-BioMed","is_official":0,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"cross-lingual-ner","task_name":"Cross-Lingual NER"},{"task_slug":"few-shot-learning","task_name":"Few-Shot Learning"},{"task_slug":"genetic-ie","task_name":"Genetic IE"},{"task_slug":"language-modeling","task_name":"Language Modeling"},{"task_slug":"language-modelling","task_name":"Language Modelling"},{"task_slug":"large-language-model","task_name":"Large Language Model"},{"task_slug":"cg","task_name":"NER"},{"task_slug":"named-entity-recognition-1","task_name":"Named Entity Recognition"},{"task_slug":"named-entity-recognition-ner","task_name":"Named Entity Recognition (NER)"},{"task_slug":"named-entity-recognition","task_name":"named-entity-recognition"}],"methods":[{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"attention-dropout","method_name":"Attention Dropout"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"cosine-annealing","method_name":"Cosine Annealing"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"gpt-3","method_name":"GPT-3"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"linear-warmup-with-cosine-annealing","method_name":"Linear Warmup With Cosine Annealing"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"weight-decay","method_name":"Weight Decay"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}