A systematic evaluation of large language models for biomedical natural language processing: benchmarks, baselines, and recommendations

• URL: http://arxiv.org/abs/2305.16326v4
• Date: Mon, 30 Sep 2024 03:11:04 GMT
• Title: A systematic evaluation of large language models for biomedical natural language processing: benchmarks, baselines, and recommendations
• Authors: Qingyu Chen, Yan Hu, Xueqing Peng, Qianqian Xie, Qiao Jin, Aidan Gilson, Maxwell B. Singer, Xuguang Ai, Po-Ting Lai, Zhizheng Wang, Vipina Kuttichi Keloth, Kalpana Raja, Jiming Huang, Huan He, Fongci Lin, Jingcheng Du, Rui Zhang, W. Jim Zheng, Ron A. Adelman, Zhiyong Lu, Hua Xu,
• Abstract summary: We present a systematic evaluation of four representative Large Language Models (LLMs) across 12 BioNLP datasets. The evaluation is conducted under four settings: zero-shot, static few-shot, dynamic K-nearest few-shot, and fine-tuning. We compare these models against state-of-the-art (SOTA) approaches that fine-tune (domain-specific) BERT or BART models.
• Score: 22.668383945059762
• License:
• Abstract: The biomedical literature is rapidly expanding, posing a significant challenge for manual curation and knowledge discovery. Biomedical Natural Language Processing (BioNLP) has emerged as a powerful solution, enabling the automated extraction of information and knowledge from this extensive literature. Recent attention has been directed towards Large Language Models (LLMs) due to their impressive performance. However, there remains a critical gap in understanding the effectiveness of LLMs in BioNLP tasks and their broader implications for method development and downstream users. Currently, there is a lack of baseline performance data, benchmarks, and practical recommendations for using LLMs in the biomedical domain. To address this gap, we present a systematic evaluation of four representative LLMs: GPT-3.5 and GPT-4 (closed-source), LLaMA 2 (open-sourced), and PMC LLaMA (domain-specific) across 12 BioNLP datasets covering six applications (named entity recognition, relation extraction, multi-label document classification, question answering, text summarization, and text simplification). The evaluation is conducted under four settings: zero-shot, static few-shot, dynamic K-nearest few-shot, and fine-tuning. We compare these models against state-of-the-art (SOTA) approaches that fine-tune (domain-specific) BERT or BART models, which are well-established methods in BioNLP tasks. The evaluation covers both quantitative and qualitative evaluations, where the latter involves manually reviewing collectively hundreds of thousands of LLM outputs for inconsistencies, missing information, and hallucinations in extractive and classification tasks. The qualitative review also examines accuracy, 1 completeness, and readability in text summarization tasks. Additionally, a cost analysis of closed-source GPT models is conducted.

Related papers

• NeuroSym-BioCAT: Leveraging Neuro-Symbolic Methods for Biomedical Scholarly Document Categorization and Question Answering [0.14999444543328289]
  We introduce a novel approach that integrates an optimized topic modelling framework, OVB-LDA, with the BI-POP CMA-ES optimization technique for enhanced scholarly document abstract categorization. We employ the distilled MiniLM model, fine-tuned on domain-specific data, for high-precision answer extraction.
  arXiv  Detail & Related papers  (2024-10-29T14:45:12Z)
• THaMES: An End-to-End Tool for Hallucination Mitigation and Evaluation in Large Language Models [0.0]
  Hallucination, the generation of factually incorrect content, is a growing challenge in Large Language Models. This paper introduces THaMES, an integrated framework and library addressing this gap. THaMES offers an end-to-end solution for evaluating and mitigating hallucinations in LLMs.
  arXiv  Detail & Related papers  (2024-09-17T16:55:25Z)
• Comparative Analysis of Open-Source Language Models in Summarizing Medical Text Data [5.443548415516227]
  Large Language Models (LLMs) have demonstrated superior performance in question answering and summarization tasks on unstructured text data. We propose an evaluation approach to analyze the performance of open-source LLMs for medical summarization tasks.
  arXiv  Detail & Related papers  (2024-05-25T16:16:22Z)
• BiomedRAG: A Retrieval Augmented Large Language Model for Biomedicine [19.861178160437827]
  Large Language Models (LLMs) have swiftly emerged as vital resources for different applications in the biomedical and healthcare domains. textscBiomedRAG attains superior performance across 5 biomedical NLP tasks. textscBiomedRAG outperforms other triple extraction systems with micro-F1 scores of 81.42 and 88.83 on GIT and ChemProt corpora, respectively.
  arXiv  Detail & Related papers  (2024-05-01T12:01:39Z)
• An Evaluation of Large Language Models in Bioinformatics Research [52.100233156012756]
  We study the performance of large language models (LLMs) on a wide spectrum of crucial bioinformatics tasks. These tasks include the identification of potential coding regions, extraction of named entities for genes and proteins, detection of antimicrobial and anti-cancer peptides, molecular optimization, and resolution of educational bioinformatics problems. Our findings indicate that, given appropriate prompts, LLMs like GPT variants can successfully handle most of these tasks.
  arXiv  Detail & Related papers  (2024-02-21T11:27:31Z)
• Zero-shot Generative Large Language Models for Systematic Review Screening Automation [55.403958106416574]
  This study investigates the effectiveness of using zero-shot large language models for automatic screening. We evaluate the effectiveness of eight different LLMs and investigate a calibration technique that uses a predefined recall threshold.
  arXiv  Detail & Related papers  (2024-01-12T01:54:08Z)
• Diversifying Knowledge Enhancement of Biomedical Language Models using Adapter Modules and Knowledge Graphs [54.223394825528665]
  We develop an approach that uses lightweight adapter modules to inject structured biomedical knowledge into pre-trained language models. We use two large KGs, the biomedical knowledge system UMLS and the novel biochemical OntoChem, with two prominent biomedical PLMs, PubMedBERT and BioLinkBERT. We show that our methodology leads to performance improvements in several instances while keeping requirements in computing power low.
  arXiv  Detail & Related papers  (2023-12-21T14:26:57Z)
• BLESS: Benchmarking Large Language Models on Sentence Simplification [55.461555829492866]
  We present BLESS, a performance benchmark of the most recent state-of-the-art large language models (LLMs) on the task of text simplification (TS) We assess a total of 44 models, differing in size, architecture, pre-training methods, and accessibility, on three test sets from different domains (Wikipedia, news, and medical) under a few-shot setting. Our evaluation indicates that the best LLMs, despite not being trained on TS, perform comparably with state-of-the-art TS baselines.
  arXiv  Detail & Related papers  (2023-10-24T12:18:17Z)
• Self-Verification Improves Few-Shot Clinical Information Extraction [73.6905567014859]
  Large language models (LLMs) have shown the potential to accelerate clinical curation via few-shot in-context learning. They still struggle with issues regarding accuracy and interpretability, especially in mission-critical domains such as health. Here, we explore a general mitigation framework using self-verification, which leverages the LLM to provide provenance for its own extraction and check its own outputs.
  arXiv  Detail & Related papers  (2023-05-30T22:05:11Z)
• Evaluation of ChatGPT Family of Models for Biomedical Reasoning and Classification [6.163540203358258]
  This study investigates the performance of large language models (LLMs) in biomedical tasks beyond question-answering. Because no patient data can be passed to the OpenAI API public interface, we evaluated model performance with over 10000 samples. We found that fine-tuning for two fundamental NLP tasks remained the best strategy.
  arXiv  Detail & Related papers  (2023-04-05T15:11:25Z)
• CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark [51.38557174322772]
  We present the first Chinese Biomedical Language Understanding Evaluation benchmark. It is a collection of natural language understanding tasks including named entity recognition, information extraction, clinical diagnosis normalization, single-sentence/sentence-pair classification. We report empirical results with the current 11 pre-trained Chinese models, and experimental results show that state-of-the-art neural models perform by far worse than the human ceiling.
  arXiv  Detail & Related papers  (2021-06-15T12:25:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.