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cs.CL2026
Gradients with Respect to Semantics Preserving Embeddings Tell the Uncertainty of Large Language Models
Mingda Li, Rundong Lv, Xinyu Li +2
Uncertainty quantification (UQ) is an important technique for ensuring the trustworthiness of LLMs, given their tendency to hallucinate. Existing state-of-the-art UQ approaches for…
cs.CL2026
Revealing Behavioral Plasticity in Large Language Models: A Token-Conditional Perspective
Liyuan Mao, Le Yu, Jing Zhou +7
In this work, we reveal that Large Language Models (LLMs) possess intrinsic behavioral plasticity-akin to chameleons adapting their coloration to environmental cues-that can be exp…
cs.CL2025
ESI: Epistemic Uncertainty Quantification via Semantic-preserving Intervention for Large Language Models
Mingda Li, Xinyu Li, Weinan Zhang +1
Uncertainty Quantification (UQ) is a promising approach to improve model reliability, yet quantifying the uncertainty of Large Language Models (LLMs) is non-trivial. In this work,…