---
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