4 papers
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…
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…
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,…
Unraveling and Mitigating Retriever Inconsistencies in Retrieval-Augmented Large Language Models
Mingda Li, Xinyu Li, Yifan Chen +2
Although Retrieval-Augmented Large Language Models (RALMs) demonstrate their superiority in terms of factuality, they do not consistently outperform the original retrieval-free Lan…