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cs.CL2025
Multi-turn Training with Basic Human Feedback Helps Little on LLM Reasoning
Qiang Liu, Wuganjing Song, Zhenzhou Lin +4
The reasoning capabilities of Large Language Models (LLMs) are typically developed through the single-turn reinforcement learning, whereas real-world applications often involve mul…
cs.CL2025
LingBench++: A Linguistically-Informed Benchmark and Reasoning Framework for Multi-Step and Cross-Cultural Inference with LLMs
Da-Chen Lian, Ri-Sheng Huang, Pin-Er Chen +7
We propose LingBench++, a linguistically-informed benchmark and reasoning framework designed to evaluate large language models (LLMs) on complex linguistic tasks inspired by the In…
cs.CL2025
Uncertainty Quantification and Confidence Calibration in Large Language Models: A Survey
Xiaoou Liu, Tiejin Chen, Longchao Da +3
Large Language Models (LLMs) excel in text generation, reasoning, and decision-making, enabling their adoption in high-stakes domains such as healthcare, law, and transportation. H…