Can LLMs predict the outcomes of machine learning experiments?
Recent advances in AI make Recursive Self-Improvement (RSI) increasingly plausible.
ProofPaper ↗
Key points
- Large Language Models (LLMs) can already propose, select, and run Machine Learning (ML) experiments.
- An LLM that proposes an experiment needs a reliable intuition of whether it is worth running given the timing constraints of long training runs.
- In this work, we measure such intuition in current frontier LLMs by evaluating how accurately they predict the impact of self-generated ML experiment ideas before these are put into training runs.
- To this end, we construct a dataset of 288 LLM-proposed ideas on eight MLE-bench tasks.
Sources (1)
- [1]Can LLMs predict the outcomes of machine learning experiments?arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 15, 07:51 PM
Recent advances in AI make Recursive Self-Improvement (RSI) increasingly plausible.
Large Language Models (LLMs) can already propose, select, and run Machine Learning (ML) experiments.
Extractive summary: sentences quoted from the sources.
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