GPT-3 Models are Poor Few-Shot Learners in the Biomedical Domain

Moradi, Milad; Blagec, Kathrin; Haberl, Florian; Samwald, Matthias

doi:10.48550/arxiv.2109.02555

Cited by 8 publications

(13 citation statements)

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“…For example, a prompt ending in “G) Delirium” will be extended into “tremens B) Dislodged otoliths” before an answer is provided. GPT-3 suffers from similar fallbacks and requires more prompt engineering to generate the desired output [ 17 ]. Additionally, the model performed far below both ChatGPT and InstructGPT on all data sets.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

How Does ChatGPT Perform on the United States Medical Licensing Examination (USMLE)? The Implications of Large Language Models for Medical Education and Knowledge Assessment

Gilson¹,

Safranek²,

Huang³

et al. 2023

JMIR Med Educ

748

392

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Background Chat Generative Pre-trained Transformer (ChatGPT) is a 175-billion-parameter natural language processing model that can generate conversation-style responses to user input. Objective This study aimed to evaluate the performance of ChatGPT on questions within the scope of the United States Medical Licensing Examination Step 1 and Step 2 exams, as well as to analyze responses for user interpretability. Methods We used 2 sets of multiple-choice questions to evaluate ChatGPT’s performance, each with questions pertaining to Step 1 and Step 2. The first set was derived from AMBOSS, a commonly used question bank for medical students, which also provides statistics on question difficulty and the performance on an exam relative to the user base. The second set was the National Board of Medical Examiners (NBME) free 120 questions. ChatGPT’s performance was compared to 2 other large language models, GPT-3 and InstructGPT. The text output of each ChatGPT response was evaluated across 3 qualitative metrics: logical justification of the answer selected, presence of information internal to the question, and presence of information external to the question. Results Of the 4 data sets, AMBOSS-Step1, AMBOSS-Step2, NBME-Free-Step1, and NBME-Free-Step2, ChatGPT achieved accuracies of 44% (44/100), 42% (42/100), 64.4% (56/87), and 57.8% (59/102), respectively. ChatGPT outperformed InstructGPT by 8.15% on average across all data sets, and GPT-3 performed similarly to random chance. The model demonstrated a significant decrease in performance as question difficulty increased (P=.01) within the AMBOSS-Step1 data set. We found that logical justification for ChatGPT’s answer selection was present in 100% of outputs of the NBME data sets. Internal information to the question was present in 96.8% (183/189) of all questions. The presence of information external to the question was 44.5% and 27% lower for incorrect answers relative to correct answers on the NBME-Free-Step1 (P<.001) and NBME-Free-Step2 (P=.001) data sets, respectively. Conclusions ChatGPT marks a significant improvement in natural language processing models on the tasks of medical question answering. By performing at a greater than 60% threshold on the NBME-Free-Step-1 data set, we show that the model achieves the equivalent of a passing score for a third-year medical student. Additionally, we highlight ChatGPT’s capacity to provide logic and informational context across the majority of answers. These facts taken together make a compelling case for the potential applications of ChatGPT as an interactive medical education tool to support learning.

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Section: Discussionmentioning

confidence: 99%

“…Zidovudine (AZT).” In the case of GPT-3, prompt engineering was necessary, with: "Please answer this multiple choice question:" + question as described previously + "Correct answer is." As GPT-3 is inherently a nondialogic model, this was necessary to reduce model hallucinations and force a clear answer [ 17 ].…”

Section: Methodsmentioning

confidence: 99%

How Does ChatGPT Perform on the United States Medical Licensing Examination (USMLE)? The Implications of Large Language Models for Medical Education and Knowledge Assessment

Gilson¹,

Safranek²,

Huang³

et al. 2023

JMIR Med Educ

748

392

View full text Add to dashboard Cite

show abstract

“…Noticing the powerful generation ability of GPT models, it is quite curious how GPT models perform on biomedical domain which is very different from general domain. However, recent works show that GPT models, even much more powerful GPT-3 model, perform poorly on biomedical tasks [11,12]. A previous work on pre-training GPT on biomedical literature is DARE [21].…”

Section: Pre-trained Language Models In Biomedical Domainmentioning

confidence: 99%

“…However, previous works mainly focus on BERT models which are more appropriate for understanding tasks, not generation tasks. In comparison, GPT models have demonstrated their abilities on generation tasks but demonstrate inferior performance when directly applying to the biomedical domain [11,12].…”

Section: Introductionmentioning

confidence: 99%

BioGPT: generative pre-trained transformer for biomedical text generation and mining

Luo

Sun

Xia

et al. 2022

Briefings in Bioinformatics

218

125

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Pre-trained language models have attracted increasing attention in the biomedical domain, inspired by their great success in the general natural language domain. Among the two main branches of pre-trained language models in the general language domain, i.e. BERT (and its variants) and GPT (and its variants), the first one has been extensively studied in the biomedical domain, such as BioBERT and PubMedBERT. While they have achieved great success on a variety of discriminative downstream biomedical tasks, the lack of generation ability constrains their application scope. In this paper, we propose BioGPT, a domain-specific generative Transformer language model pre-trained on large-scale biomedical literature. We evaluate BioGPT on six biomedical natural language processing tasks and demonstrate that our model outperforms previous models on most tasks. Especially, we get 44.98%, 38.42% and 40.76% F1 score on BC5CDR, KD-DTI and DDI end-to-end relation extraction tasks, respectively, and 78.2% accuracy on PubMedQA, creating a new record. Our case study on text generation further demonstrates the advantage of BioGPT on biomedical literature to generate fluent descriptions for biomedical terms.

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“…Few-shot learning are a subclass of machine learning approaches that draw on a small number of labeled examples. In the last few years, the emergence of large-scale language models such as BERT and GPT-3 have changed the landscape for few-shot learning, allowing the possibility of developing classifiers within only a small number of labeled examples and without any prior fine-tuning of the models [4,23,25]. In this proposed system, the authors of the scenarios would write prompts, write sample responses, and write feedback on those sample responses.…”

Section: Community Created Ai Classifiersmentioning

confidence: 99%

Digital Clinical Simulation Suite: Specifications and Architecture for Simulation-Based Pedagogy at Scale

Hillaire¹,

Waldron²,

Littenberg-Tobias³

et al. 2022

Preprint

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Role-plays of interpersonal interactions are essential to learning across professions, but effective simulations are difficult to create in typical learning management systems. To empower educators and researchers to advance simulation-based pedagogy, we have developed the Digital Clinical Simulation Suite (DCSS, pronounced “decks”), an open-source platform for rehearsing for improvisational interactions. Participants are immersed in vignettes of professional practice through video, images, and text, and they are called upon to improvisationally make difficult decisions through recorded audio and text. Tailored data displays support participant reflection, instructional facilitation, and educational research. DCSS is based on six design principles: 1) Community Adaptation, 2) Masked Technical Complexity, 3) Authenticity of Task, 4) Improvisational Voice, 5) Data Access through “5Rs”, and 6) Extensible AI Coaching. These six principles mean that any educator should be able to create a scenario that learners should engage in authentic professional challenges using ordinary computing devices, and learners and educators should have access to data for reflection, facilitation, and development of AI tools for real-time feedback. In this paper, we describe the architecture of DCSS and illustrate its use and efficacy in cases from online courses, colleges of education, and K-12 schools. Forthcoming in the Proceedings of the 2022 ACM Learning@Scale Conference

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GPT-3 Models are Poor Few-Shot Learners in the Biomedical Domain

Cited by 8 publications

References 0 publications

How Does ChatGPT Perform on the United States Medical Licensing Examination (USMLE)? The Implications of Large Language Models for Medical Education and Knowledge Assessment

How Does ChatGPT Perform on the United States Medical Licensing Examination (USMLE)? The Implications of Large Language Models for Medical Education and Knowledge Assessment

BioGPT: generative pre-trained transformer for biomedical text generation and mining

Digital Clinical Simulation Suite: Specifications and Architecture for Simulation-Based Pedagogy at Scale

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