aiqbench analysis
This research introduces an LLM-in-the-loop reinforcement learning framework specifically designed to address the curse of dimensionality in high-dimensional bioinformatics datasets. By integrating domain-informed advice and hybrid rewards from large language models, the approach improves feature selection scalability, convergence speed, and interpretability for human experts. The method demonstrates superior performance over traditional baselines while maintaining stability across various downstream machine learning models.
An LLM-in-the-loop RL Framework for Bioinformatics Feature Selection
arXiv:2610.05600v1 Announce Type: cross Abstract: High-dimensional bioinformatics data, characterized by a large number of features relative to the number of samples, pose major challenges such as the ``curse of dimensionality,'' leading to overfitting, high computational cost, and poor generalization. Traditional feature selection methods often suffer from limited scalability and adaptability in such domains. We propose an LLM-in-the-loop reinforcement learning (RL) framework for bioinformatics feature selection, where the RL agent formulates feature selection as a sequential decision-making task, while the large language model (LLM) enhances the process in two ways: (1) guiding exploration through domain-informed advice, and (2) providing hybrid rewards that integrate data-driven performance with knowledge-driven evaluation. The LLM also produces explanations to improve interpretability for human experts without altering the RL policy update. Experiments on diverse bioinformatics datasets show that the LLM-in-the-loop framework outperforms baselines, achieves stable performance across downstream models, and converges faster than pure RL.
Abstract:High-dimensional bioinformatics data, characterized by a large number of features relative to the number of samples, pose major challenges such as the ``curse of dimensionality,'' leading to overfitting, high computational cost, and poor generalization. Traditional feature selection methods often suffer from limited scalability and adaptability in such domains. We propose an LLM-in-the-loop reinforcement learning (RL) framework for bioinformatics feature selection, where the RL agent formulates feature selection as a sequential decision-making task, while the large language model (LLM) enhances the process in two ways: (1) guiding exploration through domain-informed advice, and (2) providing hybrid rewards that integrate data-driven performance with knowledge-driven evaluation. The LLM also produces explanations to improve interpretability for human experts without altering the RL policy update. Experiments on diverse bioinformatics datasets show that the LLM-in-the-loop framework outperforms baselines, achieves stable performance across downstream models, and converges faster than pure RL.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2610.05600 [cs.LG]
(or arXiv:2610.05600v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2610.05600 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 240 words
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