InvEvolve evolves white-box inventory policies from LLMs with statistical safety guarantees and outperforms classical and deep learning methods on synthetic and real retail data.
arXiv preprint arXiv:2304.06975 , year=
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TaxPraBen is a new benchmark with 14 datasets and a structured evaluation method for measuring LLM performance on Chinese real-world tax tasks and scenarios.
DoctorAgent-RL trains a Qwen2.5-7B doctor agent via multi-agent RL on the new MTMedDialog dataset to conduct dynamic, question-driven consultations, reaching 70% exact diagnostic match in real-patient trials.
APP is a multi-turn LLM framework for medical dialogue that combines empathetic questioning, Bayesian active learning, and guideline-based reasoning, outperforming baselines on a new simulated-patient benchmark in accuracy, uncertainty reduction, and user experience.
A systematic review of memory designs, evaluation methods, applications, limitations, and future directions for LLM-based agents.
A comprehensive survey of knowledge distillation for LLMs structured around algorithms, skill enhancement, and vertical applications, highlighting data augmentation as a key enabler.
A survey paper providing an overview of Large Language Models, their background, and recent advances in the field.
citing papers explorer
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InvEvolve: Evolving White-Box Inventory Policies via Large Language Models with Performance Guarantees
InvEvolve evolves white-box inventory policies from LLMs with statistical safety guarantees and outperforms classical and deep learning methods on synthetic and real retail data.
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TaxPraBen: A Scalable Benchmark for Structured Evaluation of LLMs in Chinese Real-World Tax Practice
TaxPraBen is a new benchmark with 14 datasets and a structured evaluation method for measuring LLM performance on Chinese real-world tax tasks and scenarios.
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Real-World Doctor Agent with Proactive Consultation through Multi-Agent Reinforcement Learning
DoctorAgent-RL trains a Qwen2.5-7B doctor agent via multi-agent RL on the new MTMedDialog dataset to conduct dynamic, question-driven consultations, reaching 70% exact diagnostic match in real-patient trials.
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Ask Patients with Patience: Enabling LLMs for Human-Centric Medical Dialogue with Grounded Reasoning
APP is a multi-turn LLM framework for medical dialogue that combines empathetic questioning, Bayesian active learning, and guideline-based reasoning, outperforming baselines on a new simulated-patient benchmark in accuracy, uncertainty reduction, and user experience.
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A Survey on the Memory Mechanism of Large Language Model based Agents
A systematic review of memory designs, evaluation methods, applications, limitations, and future directions for LLM-based agents.
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A Survey on Knowledge Distillation of Large Language Models
A comprehensive survey of knowledge distillation for LLMs structured around algorithms, skill enhancement, and vertical applications, highlighting data augmentation as a key enabler.
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A Comprehensive Overview of Large Language Models
A survey paper providing an overview of Large Language Models, their background, and recent advances in the field.
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