LLM s are not Zero-Shot Reasoners for Biomedical Information Extraction (2025)

Abstract: Large Language Models (LLMs) are increasingly adopted for applications in healthcare, reaching the performance of domain experts on tasks such as question answering and document summarisation. Despite their success on these tasks, it is unclear how well LLMs perform on tasks that are traditionally pursued in the biomedical domain, such as structured information extration. To bridge this gap, in this paper, we systematically benchmark LLM performance in Medical Classification and Named Entity Recognition (NER) tasks. We aim to disentangle the contribution of different factors to the performance, particularly the impact of LLMs’ task knowledge and reasoning capabilities, their (parametric) domain knowledge, and addition of external knowledge. To this end, we evaluate various open LLMs—including BioMistral and Llama-2 models—on a diverse set of biomedical datasets, using standard prompting, Chain-of-Thought (CoT) and Self-Consistency based reasoning as well as Retrieval-Augmented Generation (RAG) with PubMed and Wikipedia corpora. Counter-intuitively, our results reveal that standard prompting consistently outperforms more complex techniques across both tasks, laying bare the limitations in the current application of CoT, self-consistency and RAG in the biomedical domain. Our findings suggest that advanced prompting methods developed for knowledge- or reasoning-intensive tasks, such as CoT or RAG, are not easily portable to biomedical tasks where precise structured outputs are required. This highlights the need for more effective integration of external knowledge and reasoning mechanisms in LLMs to enhance their performance in real-world biomedical applications.

## introduction the success of large language models (llms) is reshaping ai healthcare applications, particularly in question answering @xcite , summarization @xcite @xcite , and extracting insights from unstructured patient-generated health data @xcite . while advancements in fine-tuning and in-context learning (icl) have improved llm performance, these rely on readily available structured training data @xcite @xcite . however, in biomedical contexts, such resources are often unavailable due to domain shifts @xcite or ad-hoc requirements -for example when researchers need to process a set of medical records to find patients satisfying inclusion criteria for a clinical trial @xcite (e.g., whether they're a smoker). this limits the effectiveness of parametric knowledge improvements in llms, necessitating strong zero-shot capabilities for structured prediction tasks such as biomedical classification and named entity recognition. despite this, the literature currently lacks a systematic investigation of other crucial aspects of knowledge utilization in zero-shot performance of llms in such tasks. in order to address this research gap, we first postulate that llm performance in true zeroshot settings-where only task labels and their meaningful names are provided @xcite al., 2014) existing research evaluating these factors in llms for the medical domain focus on knowledgeintensive tasks like multiple-choice qa @xcite , but their capabilities in structured prediction tasks, such as medical classification and information extraction, remain underexplored. additionally, tech-niques like zero-shot chain-of-thought (cot) reasoning @xcite , self-consistency @xcite , and retrieval-augmented generation (rag) @xcite require systematic evaluation in these contexts. additionally, evaluations often focus on proprietary models like chatgpt @xcite or @xcite , which pose challenges due to computational cost, privacy concerns, and inaccessibility for techniques like constrained decoding. despite the growing concerns regarding reliability of llms in medical applications, techniques like constrained decoding which have shown promise in mitigating llm hallucinations have not been systematically applied to medical information extraction or classification. thus, four key issues currently hinder progress: (i) reliance on training sets and parametric knowledge for structured prediction, which may be unrealistic; (ii) lack of true zero-shot evaluations for structured tasks beyond surrogate qa; (iii) dependence on large-scale, proprietary llms, limiting practical deployment. (iv) lack of a systematic analysis of impact of latest techniques such as chain-of-thought reasoning, rag and constrained generation in medical structured prediction tasks. this paper systematically benchmarks llms in medical classification and named entity recognition (ner), assessing task and external knowledge while controlling parametric knowledge. we evaluate cot reasoning, rag, and constrained generation, offering insights into their applicability. first, we present the first comprehensive benchmark of task and external knowledge adaptation for llms in medical structured prediction tasks. second, we analyze the impact of knowledge enhancement techniques, including cot, self-consistency, rag, and constrained generation. third, we demonstrate that parametric knowledge capacity, i.e., model size, is the primary driver of zero-shot performance, highlighting the limitations and potential of current llm architectures. ## related work we survey the existing benchmarking literature for the medical domain in the appendix section a, outlining the lack of studies focusing on structured prediction tasks. furthermore, we cover recent prompting techniques that were proposed to elicit reasoning in llms, and augment their domain knowledge, either by better tapping into their para-metric knowledge or by explicitly providing them with relevant external context. notably, we omit approaches that rely on existence of training sets, such as few-shot prompting @xcite or model fine-tuning, as one of the key challenges in the medical domain is the lack of annotated task data, due to privacy concerns over sharing medical records. instead, as outlines in the introduction, we focus on "true" zero-shot capabilities of llms. reasoning-and knowledge-enhancing approaches: current work attempts to improve the performance of llms from different knowledge utilization perspectives. one of the obvious methods is full parameter domain-specific pretraining @xcite . for example, @xcite propose the largest medical foundation model, trained on both biomedical and clinical data, up to 70b. @xcite , on the other hand, believe larger llms are computationally expensive to run, proposing a 2.7b llm specific for biomedical nlp tasks. when fine-tuned, the relatively small model compete with larger llms. in our study, we compare domain-generalist models with those adapted to the medical domain. since full parameter tuning is costly, many works focus on domain knowledge adaptation by pretraining @xcite or instruction tuning (willard and louf, 2023) with adapters. training-free approaches encompass chain-of-thought (cot) @xcite , self-consistency @xcite , and, concerned with lack of grounding resulting in hallucination, recent work introduce rag methods @xcite @xcite @xcite . however, most of these efforts have focused on performance in a particular knowledge paradigm and have lacked a systematic assessment of their performance on structured prediction, which we address in our study. ## methodology our methodology is designed to answer the following two research questions: to answer the first research question, we bench-mark llms on biomedical text classification and ner datasets, focusing on the "true" zero-shot setting to evaluate models' parametric knowledge. this reflects real-world scenarios where annotated data is often unavailable due to ad-hoc task requirements, resource limitations and privacy constraints @xcite . this leads to what @xcite describe as "dataset debt", highlighting issues like inconsistent documentation, lack of domain-specific information except generic entities and difficulties adapting datasets to niche domains. clinicians face significant time constraints, which limit even few-shot annotations @xcite @xcite . these factors make fine-tuning and fewshot approaches impractical for structured prediction tasks in the biomedical domain, positioning zero-shot methods as a scalable solution for realworld biomedical tasks. to answer the second question, we compare their zero-shot performance to various methods that aim to enhance task knowledge and external knowledge, while keeping the parametric knowledge static. techniques complete details of our datasets, techniques and methods are described in appendix d. ## evaluation results the complete table of results is provided in ## conclusion we provide a comprehensive benchmark and analysis of llms in medical classification and named entity recognition tasks, revealing several key insights that have significant implications for the field. we carry out a critical investigation of broad claims regarding llm capabilities by replicating them in various contexts, domains and datasets. we find that models suffer from fundamental drawbacks in generalizability, which hinder their performance in structured information extraction tasks on domain specific problems. this leads to standard prompting outperforming more advanced methods for all models. our findings underscore that parametric knowledge capacity remains the most important factor in zero-shot settings, with advanced techniques used to augment external knowledge or model reasoning failing to improve performance.