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cs.CL2025
SR: Teaching LLMs to Self-verify and Self-correct via Reinforcement Learning
Ruotian Ma, Peisong Wang, Cheng Liu +6
Recent studies have demonstrated the effectiveness of LLM test-time scaling. However, existing approaches to incentivize LLMs' deep thinking abilities generally require large-scale…
cs.CL2024★ 1 cited
Are Large Language Models Good Prompt Optimizers?
Ruotian Ma, Xiaolei Wang, Xin Zhou +5
LLM-based Automatic Prompt Optimization, which typically utilizes LLMs as Prompt Optimizers to self-reflect and refine prompts, has shown promising performance in recent studies. D…
cs.CL2024
Look Before You Leap: Towards Decision-Aware and Generalizable Tool-Usage for Large Language Models
Anchun Gui, Jian Li, Yong Dai +2
Tool-augmented large language models (LLMs) are attracting widespread attention when accessing up-to-date knowledge and alleviating hallucination issues. Nowadays, advanced closed-…