← Search

Kevin Wu

4 accepted papers

2026

Can Large Language Models Match the Conclusions of Systematic Reviews?

ICLR 2026poster

Systematic reviews (SR), in which experts summarize and analyze evidence across individual studies to provide insights on a specialized topic, are a cornerstone for evidence-based clinical decision-making, research, and policy. Given the exponential growth of scientific articles, there is growing in…

Cited by 0SourcecodeScholar
2025

AutoRedTeamer: Autonomous Red Teaming with Lifelong Attack Integration

NeurIPS 2025poster

As large language models (LLMs) become increasingly capable, security and safety evaluation are crucial. While current red teaming approaches have made strides in assessing LLM vulnerabilities, they often rely heavily on human input and lack comprehensive coverage of emerging attack vectors. This pa…

Cited by 0SourceScholar
2024

ClashEval: Quantifying the tug-of-war between an LLM’s internal prior and external evidence

NeurIPS 2024poster

Retrieval augmented generation (RAG) is frequently used to mitigate hallucinations and provide up-to-date knowledge for large language models (LLMs). However, given that document retrieval is an imprecise task and sometimes results in erroneous or even harmful content being presented in context, thi…

2024

DataInf: Efficiently Estimating Data Influence in LoRA-tuned LLMs and Diffusion Models

ICLR 2024poster

Quantifying the impact of training data points is crucial for understanding the outputs of machine learning models and for improving the transparency of the AI pipeline. The influence function is a principled and popular data attribution method, but its computational cost often makes it challenging…