| --- |
| license: mit |
| task_categories: |
| - question-answering |
| - text-generation |
| language: |
| - en |
| tags: |
| - science |
| - physics |
| - biology |
| - chemistry |
| - experimental-prediction |
| - benchmark |
| size_categories: |
| - n<1K |
| --- |
| |
| # SciPredict: Can LLMs Predict the Outcomes of Research Experiments? |
|
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| **Paper:** SciPredict: Can LLMs Predict the Outcomes of Research Experiments in Natural Sciences? |
|
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| ## Overview |
|
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| SciPredict is a benchmark evaluating whether AI systems can predict experimental outcomes in physics, biology, and chemistry. The dataset comprises **405 questions** derived from recently published empirical studies (post-March 2025), spanning **33 subdomains**. |
|
|
| ## Dataset Structure |
|
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| - **Total Questions:** 405 (5,716 rows including model responses) |
| - **Domains:** Physics (9 subdomains), Chemistry (10 subdomains), Biology (14 subdomains) |
| - **Question Formats:** Multiple-choice (MCQ), Free-format, Numerical |
|
|
| ### Key Fields |
|
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| - `DOMAIN`: Scientific domain (Physics, Biology, Chemistry) |
| - `FIELD`: Specific field within the domain |
| - `PQ_FORMAT`: Question format (MCQ, Free-Format, Numerical) |
| - `TITLE`: Paper title |
| - `URL`: Paper URL |
| - `PUBLISHING_DATE`: Publication date |
| - `EXPERIMENTAL_SETUP`: Description of the experimental configuration |
| - `MEASUREMENT_TAKEN`: What was measured in the experiment |
| - `OUTCOME_PREDICTION_QUESTION`: The prediction task |
| - `GTA`: Ground truth answer |
| - `BACKGROUND_KNOWLEDGE`: Expert-curated background knowledge |
| - `RELATED_PAPERS_DATA`: Related papers information |
|
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| ## Key Findings |
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| - **Model accuracy:** 14-26% (vs. ~20% human expert accuracy) |
| - **Poor calibration:** Models cannot distinguish reliable from unreliable predictions |
| - **Background knowledge helps:** Providing expert-curated context improves performance |
| - **Format matters:** Performance degrades from MCQ → Free-form → Numerical |
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|
