2 citations · 5 across the 12 of their papers we have counts for
7 papers · 1 filter
Diagnosing Multi-step Reasoning Failures in Black-box LLMs via Stepwise Confidence Attribution
Xiaoou Liu, Tiejin Chen, Dengjia Zhang +3
Large Language Models have achieved strong performance on reasoning tasks with objective answers by generating step-by-step solutions, but diagnosing where a multi-step reasoning t…
Position: Uncertainty Quantification in LLMs is Just Unsupervised Clustering
Tiejin Chen, Longchao Da, Xiaoou Liu +1
Uncertainty Quantification (UQ) is widely regarded as the primary safeguard for deploying Large Language Models (LLMs) in high-stakes domains. However, we argue that the field suff…
LangMARL: Natural Language Multi-Agent Reinforcement Learning
Huaiyuan Yao, Longchao Da, Xiaoou Liu +3
Large language model (LLM) agents struggle to autonomously evolve coordination strategies in dynamic environments, largely because coarse global outcomes obscure the causal signals…
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…
Uncertainty Quantification of Large Language Models through Multi-Dimensional Responses
Tiejin Chen, Xiaoou Liu, Longchao Da +3
Large Language Models (LLMs) have demonstrated remarkable capabilities across various tasks due to large training datasets and powerful transformer architecture. However, the relia…
MCQA-Eval: Efficient Confidence Evaluation in NLG with Gold-Standard Correctness Labels
Xiaoou Liu, Zhen Lin, Longchao Da +3
Large Language Models (LLMs) require robust confidence estimation, particularly in critical domains like healthcare and law where unreliable outputs can lead to significant consequ…
Understanding the Uncertainty of LLM Explanations: A Perspective Based on Reasoning Topology
Longchao Da, Xiaoou Liu, Jiaxin Dai +3
Understanding the uncertainty in large language model (LLM) explanations is important for evaluating their faithfulness and reasoning consistency, and thus provides insights into t…