5 papers
DUD: Decoupled Update Dynamics for Reliable Uncertainty Quantification in Large Language Models
Yixin Bu, Runze Xia, Guanyun Zou +3
Accurate Uncertainty Quantification (UQ) is critical for reliable deployment of Large Language Models (LLMs), yet traditional probability-based metrics often fail to capture the mo…
Diversity-Oriented Fine-Tuning for Uncertainty-Based Hallucination Detection
Qiuyuan Li, Hongliang Dai, Piji Li
Existing hallucination detection methods are typically conducted at the inference stage, without making any modifications to the model itself. In this paper, we are interested in e…
CRISP: Compressing Redundancy in Chain-of-Thought via Intrinsic Saliency Pruning
Yangsong Lan, Hongliang Dai, Piji Li
Long Chain-of-Thought (CoT) reasoning is pivotal for the success of recent reasoning models but suffers from high computational overhead and latency. While prior works attempt to c…
M-BRe: Discovering Training Samples for Relation Extraction from Unlabeled Texts with Large Language Models
Zexuan Li, Hongliang Dai, Piji Li
For Relation Extraction (RE), the manual annotation of training data may be prohibitively expensive, since the sentences that contain the target relations in texts can be very scar…
Generating Diverse Training Samples for Relation Extraction with Large Language Models
Zexuan Li, Hongliang Dai, Piji Li
Using Large Language Models (LLMs) to generate training data can potentially be a preferable way to improve zero or few-shot NLP tasks. However, many problems remain to be investig…