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Eric Wu

2 accepted papers

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…