{"doi":"10.1101/2023.12.19.572483","title":"A benchmark for large language models in bioinformatics","abstract":"Abstract The rapid advancements in artificial intelligence, particularly in Large Language Models (LLMs) such as GPT-4, Gemini, and LLaMA, have opened new avenues for computational biology and bioinformatics. We report the development of BioLLMBench, a novel framework designed to evaluate LLMs in bioinformatics tasks. This study assessed GPT-4, Gemini, and LLaMA through 2,160 experimental runs, focusing on 24 distinct tasks across six key areas: domain expertise, mathematical problem-solving, coding proficiency, data visualization, research paper summarization, and machine learning model development. Tasks ranged from fundamental to expert-level challenges, and each area was evaluated using seven specific metrics. A Contextual Response Variability Analysis was implemented to understand how model responses varied under different conditions. Results showed diverse performance: GPT-4 led in most tasks, achieving a 91.3% proficiency in domain knowledge, while Gemini excelled in mathematical problem-solving with a 97.5% proficiency score. GPT-4 also outperformed in machine learning model development, though Gemini and LLaMA struggled to generate executable code. All models faced challenges in research paper summarization, scoring below 40% using the ROUGE metric. Model performance variance increased when using a new chat window, though average scores remained similar. The study also discusses the limitations and potential misuse risks of these models in bioinformatics.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2023,"id":390876,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":9,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.757,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":56514,"name":"Gaia Andreoletti","orcid":"0000-0002-0452-0009","position":1,"is_corresponding":false},{"id":32450,"name":"Viorel Munteanu","orcid":"0000-0002-4133-5945","position":2,"is_corresponding":false},{"id":1163706,"name":"Ariel Suhodolschi","orcid":null,"position":3,"is_corresponding":false},{"id":62290,"name":"Dumitru Ciorbă","orcid":"0000-0002-3157-5072","position":4,"is_corresponding":false},{"id":62291,"name":"Viorel Bostan","orcid":"0000-0002-2422-3538","position":5,"is_corresponding":false},{"id":32455,"name":"Mihai Dimian","orcid":"0000-0002-2093-8659","position":6,"is_corresponding":false},{"id":15613,"name":"Eleazar Eskin","orcid":"0000-0003-1149-4758","position":7,"is_corresponding":false},{"id":1163116,"name":"Wei Wang","orcid":"0000-0002-5934-4268","position":8,"is_corresponding":false},{"id":6600,"name":"Serghei Mangul","orcid":"0000-0003-4770-3443","position":9,"is_corresponding":false},{"id":857493,"name":"Varuni Sarwal","orcid":"0000-0001-7563-9835","position":0,"is_corresponding":true}],"reference_count":22,"raw_metadata":null,"created_at":"2026-07-19T01:18:42.653719Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}