PubMedQA
Models answer biomedical research questions with yes, no, or maybe, given a PubMed abstract without its conclusion. Scored by accuracy.
In the authors’ words
We introduce PubMedQA, a novel biomedical question answering (QA) dataset collected from PubMed abstracts. The task of PubMedQA is to answer research questions with yes/no/maybe (e.g.: Do preoperative statins reduce atrial fibrillation after coronary artery bypass grafting?) using the corresponding abstracts. PubMedQA has 1k expert-annotated, 61.2k unlabeled and 211.3k artificially generated QA instances. Each PubMedQA instance is composed of (1) a question which is either an existing research article title or derived from one, (2) a context which is the corresponding abstract without its conclusion, (3) a long answer, which is the conclusion of the abstract and, presumably, answers the research question, and (4) a yes/no/maybe answer which summarizes the conclusion. PubMedQA is the first QA dataset where reasoning over biomedical research texts, especially their quantitative contents, is required to answer the questions. Our best performing model, multi-phase fine-tuning of BioBERT with long answer bag-of-word statistics as additional supervision, achieves 68.1% accuracy, compared to single human performance of 78.0% accuracy and majority-baseline of 55.2% accuracy, leaving much room for improvement. PubMedQA is publicly available at https://pubmedqa.github.io.
- Level
- University Undergraduates and entry-level professionals
- Scored
- Checked automatically A program checks the result against an answer key, unit tests or the expected final state.
- Answers
- Multiple choice
Claude 3 Sonnet leads with 78.3% among models released by Mar 2024
Best score per model as labs report them in their own cards. Labs pick their own settings, so these compare less cleanly than independent runs.
The newest model with a score here came out in Mar 2024, and nobody has reported a newer one. Among the 20 highest-ranked models on the capability index, these have no score yet:
- GPT-6 Astra
- Claude Opus 5.5
- GPT-6.1 Terra
- GPT-6.1 Sol
- and 16 more
- 1 Claude 3 Sonnet 78.3%
- 2 Claude 3 Haiku 76%
- 3 Claude 3 Opus 75.8%
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | Claude 3 Sonnet | 78.3% | Mar 2024 | Model card | |
| 2 | Claude 3 Haiku | 76% | Mar 2024 | Model card | |
| 3 | Claude 3 Opus | 75.8% | Mar 2024 | Model card |
The benchmark's own leaderboard has 24 entries, 3 of them models we track. We plot every source in the charts below.
2 models have held number one since 2023
Lab claims count from the day the card came out, independent runs from the model’s release. We draw one row each for the benchmark’s own leaderboard and what labs claim in their cards.
Scores over time
All the scores we have, one dot each, so a model tested at several settings appears more than once.
No launch post has listed it since Mar 2024
Launch posts from every lab that has listed it, one tick per post. Tall colored ticks include it, and short gray ones leave it out.
| Date | Launch post | Score | As the post writes it |
|---|---|---|---|
| 4 Mar 2024 | Claude 3 Opus | 75.8 | PubMedQA (5-shot) |
3 system cards report it, from 1 lab
Besides launch posts, we read the system cards, model cards and technical reports labs publish with a release. They report far more, including safety and frontier-risk tests.
| Date | Document | Score | As the document writes it | Section |
|---|---|---|---|---|
| 4 Mar 2024 | Claude 3 Sonnet model card | 78.3 | PubMedQA, 5-shot. 0-shot 79.7 (Table 1) | Capabilities |
| 4 Mar 2024 | Claude 3 Opus model card | 75.8 | PubMedQA, 5-shot. 0-shot 74.9 (Table 1) | Capabilities |
| 4 Mar 2024 | Claude 3 Haiku model card | 76 | PubMedQA, 5-shot. 0-shot 78.5 (Table 1) | Capabilities |
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