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README.md

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----
-license: cc-by-nc-4.0
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+---
+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?
+
+**Paper:** SciPredict: Can LLMs Predict the Outcomes of Research Experiments in Natural Sciences?
+
+## Overview
+
+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
+
+- **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
+
+- `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
+
+## Key Findings
+
+- **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