3 citations · 6 across the 12 of their papers we have counts for
13 papers
From Passive Metric to Active Signal: The Evolving Role of Uncertainty Quantification in Large Language Models
Jiaxin Zhang, Wendi Cui, Zhuohang Li +4
While Large Language Models (LLMs) show remarkable capabilities, their unreliability remains a critical barrier to deployment in high-stakes domains. This survey charts a functiona…
Judging with Confidence: Calibrating Autoraters to Preference Distributions
Zhuohang Li, Xiaowei Li, Chengyu Huang +11
The alignment of large language models (LLMs) with human values increasingly relies on using other LLMs as automated judges, or ``autoraters''. However, their reliability is limite…
What Really is a Member? Discrediting Membership Inference via Poisoning
Neal Mangaokar, Ashish Hooda, Zhuohang Li +5
Membership inference tests aim to determine whether a particular data point was included in a language model's training set. However, recent works have shown that such tests often…
SCE: Scalable Consistency Ensembles Make Blackbox Large Language Model Generation More Reliable
Jiaxin Zhang, Zhuohang Li, Wendi Cui +3
Large language models (LLMs) have demonstrated remarkable performance, yet their diverse strengths and weaknesses prevent any single LLM from achieving dominance across all tasks.…
Towards Statistical Factuality Guarantee for Large Vision-Language Models
Zhuohang Li, Chao Yan, Nicholas J. Jackson +4
Advancements in Large Vision-Language Models (LVLMs) have demonstrated promising performance in a variety of vision-language tasks involving image-conditioned free-form text genera…
A Survey of Automatic Prompt Optimization with Instruction-focused Heuristic-based Search Algorithm
Wendi Cui, Zhuohang Li, Hao Sun +5
Recent advances in Large Language Models have led to remarkable achievements across a variety of Natural Language Processing tasks, making prompt engineering increasingly central t…
Scale-up Unlearnable Examples Learning with High-Performance Computing
Yanfan Zhu, Issac Lyngaas, Murali Gopalakrishnan Meena +8
Recent advancements in AI models are structured to retain user interactions, which could inadvertently include sensitive healthcare data. In the healthcare field, particularly when…
Do You Know What You Are Talking About? Characterizing Query-Knowledge Relevance For Reliable Retrieval Augmented Generation
Zhuohang Li, Jiaxin Zhang, Chao Yan +4
Language models (LMs) are known to suffer from hallucinations and misinformation. Retrieval augmented generation (RAG) that retrieves verifiable information from an external knowle…
Exploring User-level Gradient Inversion with a Diffusion Prior
Zhuohang Li, Andrew Lowy, Jing Liu +4
We explore user-level gradient inversion as a new attack surface in distributed learning. We first investigate existing attacks on their ability to make inferences about private in…
Analyzing Inference Privacy Risks Through Gradients in Machine Learning
Zhuohang Li, Andrew Lowy, Jing Liu +4
In distributed learning settings, models are iteratively updated with shared gradients computed from potentially sensitive user data. While previous work has studied various privac…
SEE: Strategic Exploration and Exploitation for Cohesive In-Context Prompt Optimization
Wendi Cui, Zhuohang Li, Hao Sun +5
Designing optimal prompts for Large Language Models (LLMs) is a complicated and resource-intensive task, often requiring substantial human expertise and effort. Existing approaches…
DCR-Consistency: Divide-Conquer-Reasoning for Consistency Evaluation and Improvement of Large Language Models
Wendi Cui, Jiaxin Zhang, Zhuohang Li +4
Evaluating the quality and variability of text generated by Large Language Models (LLMs) poses a significant, yet unresolved research challenge. Traditional evaluation methods, suc…
SAC3: Reliable Hallucination Detection in Black-Box Language Models via Semantic-aware Cross-check Consistency
Jiaxin Zhang, Zhuohang Li, Kamalika Das +2
Hallucination detection is a critical step toward understanding the trustworthiness of modern language models (LMs). To achieve this goal, we re-examine existing detection approach…