NASA’s new dark energy space telescope can also detect killer asteroids
technologyreview.com·2h ago
AI Research
arXiv CS.AI·Jun 12·5 min read
SciR: A Controllable Benchmark for Scientific Reasoning in LLMs
TL;DR
Evaluating large language models (LLMs) on scientific reasoning is challenging due to the lack of reliable benchmarks. SciR is introduced as a controllable benchmark that assesses LLMs on deduction, induction, and causal abduction through structured tasks.
✦ Why It Matters
Engineers can use SciR to better evaluate and improve LLMs for scientific applications.
Key Takeaways
- Utilize SciR for benchmarking LLMs in scientific contexts.
- Focus on both extraction and inference difficulties in model evaluation.
- Explore the performance profiles of reasoning vs. non-reasoning models.
- Apply findings to enhance LLM capabilities in scientific reasoning tasks.
Full Summary
Current benchmarks for evaluating large language models (LLMs) in scientific reasoning are inadequate, as they often rely on costly human annotations or synthetic tasks that do not reflect real scientific discourse. SciR is a new benchmark designed to address this gap by combining multi-paradigm reasoning with controllable scientific rendering, focusing on three key types of inference: deduction, induction, and causal abduction.
Tasks are generated from formal structures like deduction trees and causal graphs, ensuring verifiable answers. The benchmark allows for independent variation of two difficulty axes: the complexity of extracting necessary information and the difficulty of the reasoning process itself.
Testing six different models showed that both difficulty axes adversely affected performance, with reasoning models generally outperforming non-reasoning models on inference tasks. SciR represents a significant advancement in the evaluation of LLMs, providing a structured approach to assess their scientific reasoning capabilities.