3 accepted papers
Large Language Models (LLMs) have demonstrated impressive capabilities in text generation but raise concerns regarding potential copyright infringement. While prior research has explored mitigation strategies like content filtering and alignment, the impact of adversarial persuasion techniques in el
Zeroth-order optimization (ZO) is a memory-efficient strategy for fine-tuning Large Language Models using only forward passes. However, applying ZO fine-tuning in memory-constrained settings such as mobile phones and laptops remains challenging since these settings often involve weight quantization,…
Given a Large Language Model (LLM) generation, how can we identify which training data led to this generation? In this paper, we proposed RapidIn, a scalable framework adapting to LLMs for estimating the influence of each training data. The proposed framework consists of two stages: caching and retr…