GRIMIP integrates LLMs as probabilistic surrogates inside Bayesian optimization to perform instance-specific MIP solver configuration and reports over 40% reduction in primal-dual integral on hard benchmark instances.
arXiv preprint arXiv:2304.05341 , year=
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GRIMIP: A General Framework for Instance-Specific Configuration of MIP Solvers Using LLMs
GRIMIP integrates LLMs as probabilistic surrogates inside Bayesian optimization to perform instance-specific MIP solver configuration and reports over 40% reduction in primal-dual integral on hard benchmark instances.
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Unleashing LLMs in Bayesian Optimization: Preference-Guided Framework for Scientific Discovery
LGBO integrates LLM semantic preferences continuously into Bayesian optimization iterations, with a theoretical worst-case guarantee and empirical gains including 90% of best value in 6 iterations on a wet-lab battery task.
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LILO: Bayesian Optimization with Natural Language Feedback
LILO integrates LLMs to translate natural language feedback into preference signals for Gaussian process-based Bayesian optimization, outperforming standard preference BO and LLM-only methods on benchmarks.
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ChemCrow: Augmenting large-language models with chemistry tools
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Efficient and Principled Scientific Discovery through Bayesian Optimization: A Tutorial
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