AutoSDT: Scaling Data-Driven Discovery Tasks Toward Open Co-Scientists

TL;DR

AutoSDT introduces an automated pipeline to create a large, high-quality dataset of scientific discovery tasks using LLMs, significantly improving AI co-scientist capabilities and benchmarking performance.

Contribution

The paper presents AutoSDT, a novel automatic data collection pipeline that builds the largest open dataset for data-driven scientific discovery and enhances LLM performance on related tasks.

Findings

AutoSDT-5K dataset contains 5,404 tasks across four scientific disciplines.

93% of collected tasks are ecologically valid, with 92.2% of programs functionally correct.

AutoSDT-Coder models outperform baseline models and approach GPT-4o performance on key benchmarks.

Abstract

Despite long-standing efforts in accelerating scientific discovery with AI, building AI co-scientists remains challenging due to limited high-quality data for training and evaluation. To tackle this data scarcity issue, we present AutoSDT, an automatic pipeline that collects high-quality coding tasks in real-world data-driven discovery workflows. AutoSDT leverages the coding capabilities and parametric knowledge of LLMs to search for diverse sources, select ecologically valid tasks, and synthesize accurate task instructions and code solutions. Using our pipeline, we construct AutoSDT-5K, a dataset of 5,404 coding tasks for data-driven discovery that covers four scientific disciplines and 756 unique Python packages. To the best of our knowledge, AutoSDT-5K is the only automatically collected and the largest open dataset for data-driven scientific discovery. Expert feedback on a subset of 256 tasks shows the effectiveness of AutoSDT: 93% of the collected tasks are ecologically valid, and 92.2% of the synthesized programs are functionally correct. Trained on AutoSDT-5K, the Qwen2.5-Coder-Instruct LLM series, dubbed AutoSDT-Coder, show substantial improvement on two challenging data-driven discovery benchmarks, ScienceAgentBench and DiscoveryBench. Most notably, AutoSDT-Coder-32B reaches the same level of performance as GPT-4o on ScienceAgentBench with a success rate of 7.8%, doubling the performance of its base model. On DiscoveryBench, it lifts the hypothesis matching score to 8.1, bringing a 17.4% relative improvement and closing the gap between open-weight models and GPT-4o.