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--- |
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license: mit |
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task_categories: |
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- question-answering |
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- text-generation |
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language: |
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- en |
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tags: |
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- science |
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- physics |
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- biology |
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- chemistry |
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- experimental-prediction |
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- benchmark |
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size_categories: |
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- n<1K |
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--- |
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# 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**. |
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## Dataset Structure |
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- **Total Questions:** 405 (5,716 rows including model responses) |
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- **Domains:** Physics (9 subdomains), Chemistry (10 subdomains), Biology (14 subdomains) |
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- **Question Formats:** Multiple-choice (MCQ), Free-format, Numerical |
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### Key Fields |
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- `DOMAIN`: Scientific domain (Physics, Biology, Chemistry) |
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- `FIELD`: Specific field within the domain |
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- `PQ_FORMAT`: Question format (MCQ, Free-Format, Numerical) |
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- `TITLE`: Paper title |
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- `URL`: Paper URL |
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- `PUBLISHING_DATE`: Publication date |
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- `EXPERIMENTAL_SETUP`: Description of the experimental configuration |
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- `MEASUREMENT_TAKEN`: What was measured in the experiment |
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- `OUTCOME_PREDICTION_QUESTION`: The prediction task |
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- `GTA`: Ground truth answer |
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- `BACKGROUND_KNOWLEDGE`: Expert-curated background knowledge |
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- `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) |
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- **Poor calibration:** Models cannot distinguish reliable from unreliable predictions |
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- **Background knowledge helps:** Providing expert-curated context improves performance |
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- **Format matters:** Performance degrades from MCQ → Free-form → Numerical |
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