LLM agents iteratively generate and optimize data processing strategies for fine-tuning, delivering over 80% win rates versus unprocessed data and 65% versus LLM-based AutoML baselines while cutting search time by up to 10x.
Baichuan-m1: Pushing the medical capability of large language models
5 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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Multi-turn evidence seeking reduces LLM diagnostic accuracy by 12.75% and supporting-evidence quality by 24.36% versus full-context evaluation in a new OSCE-inspired benchmark across 468 cases and 15 models.
ReMedi boosts LLM performance on EHR clinical predictions by up to 19.9% F1 through ground-truth-guided rationale regeneration and fine-tuning.
LLMs show strong exam performance on medical tasks but exhibit a clear gap in accuracy on authentic clinical decision-making as measured by the new MR-Bench benchmark and unified evaluations.
A survey compiling RL methods, challenges, data resources, and applications for enhancing reasoning in large language models and large reasoning models since DeepSeek-R1.
citing papers explorer
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LLM-AutoDP: Automatic Data Processing via LLM Agents for Model Fine-tuning
LLM agents iteratively generate and optimize data processing strategies for fine-tuning, delivering over 80% win rates versus unprocessed data and 65% versus LLM-based AutoML baselines while cutting search time by up to 10x.
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Active Evidence-Seeking and Diagnostic Reasoning in Large Language Models for Clinical Decision Support
Multi-turn evidence seeking reduces LLM diagnostic accuracy by 12.75% and supporting-evidence quality by 24.36% versus full-context evaluation in a new OSCE-inspired benchmark across 468 cases and 15 models.
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ReMedi: Reasoner for Medical Clinical Prediction
ReMedi boosts LLM performance on EHR clinical predictions by up to 19.9% F1 through ground-truth-guided rationale regeneration and fine-tuning.