47 citations · 100 across the 14 of their papers we have counts for
6 papers · 1 filter
Detecting and Correcting Reference Hallucinations in Commercial LLMs and Deep Research Agents
Delip Rao, Eric Wong, Chris Callison-Burch
Large language models and deep research agents supply citation URLs to support their claims, yet the reliability of these citations has not been systematically measured. We address…
Jailbreak Distillation: Renewable Safety Benchmarking
Jingyu Zhang, Ahmed Elgohary, Xiawei Wang +5
Large language models (LLMs) are rapidly deployed in critical applications, raising urgent needs for robust safety benchmarking. We propose Jailbreak Distillation (JBDistill), a no…
Adaptively profiling models with task elicitation
Davis Brown, Prithvi Balehannina, Helen Jin +3
Language model evaluations often fail to characterize consequential failure modes, forcing experts to inspect outputs and build new benchmarks. We introduce task elicitation, a met…
Avoiding Copyright Infringement via Large Language Model Unlearning
Guangyao Dou, Zheyuan Liu, Qing Lyu +2
Pre-trained Large Language Models (LLMs) have demonstrated remarkable capabilities but also pose risks by learning and generating copyrighted material, leading to significant legal…
Defending Large Language Models against Jailbreak Attacks via Semantic Smoothing
Jiabao Ji, Bairu Hou, Alexander Robey +5
Aligned large language models (LLMs) are vulnerable to jailbreaking attacks, which bypass the safeguards of targeted LLMs and fool them into generating objectionable content. While…
Assessing biomedical knowledge robustness in large language models by query-efficient sampling attacks
R. Patrick Xian, Alex J. Lee, Satvik Lolla +4
The increasing depth of parametric domain knowledge in large language models (LLMs) is fueling their rapid deployment in real-world applications. Understanding model vulnerabilitie…