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From the 1 of 18 linked papers with an AI index.

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20242026
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cs.LG2026

LLM-AutoDP: Automatic Data Processing via LLM Agents for Model Fine-tuning

Wei Huang, Anda Cheng, Yinggui Wang +2

Large Language Models (LLMs) can be fine-tuned on domain-specific data to enhance their performance in specialized fields. However, such data often contains numerous low-quality sa…

cs.LG2025

Mitigating Catastrophic Forgetting in Large Language Models with Forgetting-aware Pruning

Wei Huang, Anda Cheng, Yinggui Wang

Recent advancements in large language models (LLMs) have shown impressive capabilities in various downstream tasks but typically face Catastrophic Forgetting (CF) during fine-tunin…

cs.LG2025

DPF-CM: A Data Processing Framework with Privacy-Preserving Vector Databases for Chinese Medical LLMs Training and Deployment

Wei Huang, Anda Cheng, Zhao Zhang +1

Current open-source training pipelines for Chinese medical language models predominantly emphasize optimizing training methodologies to enhance the performance of large language mo…

cs.LG2025

Transferable Adversarial Examples with Bayes Approach

Mingyuan Fan, Cen Chen, Wenmeng Zhou +1

The vulnerability of deep neural networks (DNNs) to black-box adversarial attacks is one of the most heated topics in trustworthy AI. In such attacks, the attackers operate without…

cs.LG2024

Information Leakage from Embedding in Large Language Models

Zhipeng Wan, Anda Cheng, Yinggui Wang +1

The widespread adoption of large language models (LLMs) has raised concerns regarding data privacy. This study aims to investigate the potential for privacy invasion through input…