1 citations · 1 across the 4 of their papers we have counts for
5 papers
Alleviating Hallucination in Reasoning Tasks with Training-Free Uncertainty-Guided Steering
Litian Liu, Qiqi Hou, Yubing Jian +5
Recent work on hallucination detection in large language models has shown that, for a fixed pre-trained model and reasoning task, it is possible to estimate the model's confidence…
From Out-of-Distribution Detection to Hallucination Detection: A Geometric View
Litian Liu, Reza Pourreza, Yubing Jian +2
Detecting hallucinations in large language models is a critical open problem with significant implications for safety and reliability. While existing hallucination detection method…
Enhancing Hallucination Detection through Noise Injection
Litian Liu, Reza Pourreza, Sunny Panchal +4
Large Language Models (LLMs) are prone to generating plausible yet incorrect responses, known as hallucinations. Effectively detecting hallucinations is therefore crucial for the s…
Fast Decision Boundary based Out-of-Distribution Detector
Litian Liu, Yao Qin
Efficient and effective Out-of-Distribution (OOD) detection is essential for the safe deployment of AI systems. Existing feature space methods, while effective, often incur signifi…
Detecting Out-of-Distribution Through the Lens of Neural Collapse
Litian Liu, Yao Qin
Out-of-Distribution (OOD) detection is critical for safe deployment; however, existing detectors often struggle to generalize across datasets of varying scales and model architectu…