On LLM-Based Scientific Inductive Reasoning Beyond Equations
This work addresses the challenge of improving LLMs' inductive reasoning capabilities for scientific discovery, but it is incremental as it focuses on benchmarking rather than proposing a new method.
The paper tackles the problem of enabling large language models (LLMs) to perform inductive reasoning in novel scientific environments without relying on explicit equations, by introducing a new benchmark called SIRBench-V1. The result shows that current LLMs struggle with this task, highlighting its difficulty and the need for further advancement.
As large language models (LLMs) increasingly exhibit human-like capabilities, a fundamental question emerges: How can we enable LLMs to learn the underlying patterns from limited examples in entirely novel environments and apply them effectively? This question is central to the ability of LLMs in inductive reasoning. Existing research on LLM-based inductive reasoning can be broadly categorized based on whether the underlying rules are expressible via explicit mathematical equations. However, many recent studies in the beyond-equations category have emphasized rule design without grounding them in specific scenarios. Inspired by the parallels between inductive reasoning and human scientific discovery, we propose the task of LLM-Based Scientific Inductive Reasoning Beyond Equations and introduce a new benchmark, SIRBench-V1, to evaluate the inductive reasoning abilities of LLMs in scientific settings. Our experimental results show that current LLMs still struggle with this task, underscoring its difficulty and the need for further advancement in this area.