54 citations · 83 across the 8 of their papers we have counts for
10 papers
ConfTuner: Training Large Language Models to Express Their Confidence Verbally
Yibo Li, Miao Xiong, Jiaying Wu +1
Large Language Models (LLMs) are increasingly deployed in high-stakes domains such as science, law, and healthcare, where accurate expressions of uncertainty are essential for reli…
MLR-Bench: Evaluating AI Agents on Open-Ended Machine Learning Research
Hui Chen, Miao Xiong, Yujie Lu +7
Recent advancements in AI agents have demonstrated their growing potential to drive and support scientific discovery. In this work, we introduce MLR-Bench, a comprehensive benchmar…
ID: Identity-Preserving-yet-Diversified Diffusion Models for Synthetic Face Recognition
Shen Li, Jianqing Xu, Jiaying Wu +7
Synthetic face recognition (SFR) aims to generate synthetic face datasets that mimic the distribution of real face data, which allows for training face recognition models in a priv…
FlipAttack: Jailbreak LLMs via Flipping
Yue Liu, Xiaoxin He, Miao Xiong +5
This paper proposes a simple yet effective jailbreak attack named FlipAttack against black-box LLMs. First, from the autoregressive nature, we reveal that LLMs tend to understand t…
Prompt-and-Align: Prompt-Based Social Alignment for Few-Shot Fake News Detection
Jiaying Wu, Shen Li, Ailin Deng +2
Despite considerable advances in automated fake news detection, due to the timely nature of news, it remains a critical open question how to effectively predict the veracity of new…
Can LLMs Express Their Uncertainty? An Empirical Evaluation of Confidence Elicitation in LLMs
Miao Xiong, Zhiyuan Hu, Xinyang Lu +4
Empowering large language models to accurately express confidence in their answers is essential for trustworthy decision-making. Previous confidence elicitation methods, which prim…
Proximity-Informed Calibration for Deep Neural Networks
Miao Xiong, Ailin Deng, Pang Wei Koh +4
Confidence calibration is central to providing accurate and interpretable uncertainty estimates, especially under safety-critical scenarios. However, we find that existing calibrat…
GraphCleaner: Detecting Mislabelled Samples in Popular Graph Learning Benchmarks
Yuwen Li, Miao Xiong, Bryan Hooi
Label errors have been found to be prevalent in popular text, vision, and audio datasets, which heavily influence the safe development and evaluation of machine learning algorithms…
Great Models Think Alike: Improving Model Reliability via Inter-Model Latent Agreement
Ailin Deng, Miao Xiong, Bryan Hooi
Reliable application of machine learning is of primary importance to the practical deployment of deep learning methods. A fundamental challenge is that models are often unreliable…
Birds of a Feather Trust Together: Knowing When to Trust a Classifier via Adaptive Neighborhood Aggregation
Miao Xiong, Shen Li, Wenjie Feng +3
How do we know when the predictions made by a classifier can be trusted? This is a fundamental problem that also has immense practical applicability, especially in safety-critical…