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GuardAgent: Safeguard LLM Agents by a Guard Agent via Knowledge-Enabled Reasoning

The first automated guardrail for agents.

Extracting and Understanding the Superficial Knowledge in Alignment

We examined how superficial LLM alignments are thru a linear distillation method.

GuideLLM: Exploring LLM-Guided Conversation with Applications in Autobiography Interviewing

We develop a chatbot for reminiscence therapy

LLM-PBE: Assessing Data Privacy in Large Language Models

A comprehensive privacy assessment of LLMs.

Decoding Compressed Trust: Scrutinizing the Trustworthiness of Efficient LLMs Under Compression

A comprehensive trustworthiness assessment of compressed LLMs.

Revisiting Zeroth-Order Optimization for Memory-Efficient LLM Fine-Tuning: A Benchmark

Zeroth-order optimization for LLM.

On the Generalization Ability of Unsupervised Pretraining

Recent advances in unsupervised learning have shown that unsupervised pre-training, followed by fine-tuning, can improve model generalization. However, a rigorous understanding of how the representation function learned on an unlabeled dataset …

Safe and Robust Watermark Injection with a Single OoD Image

A new method for safely and robustly injecting watermark after training without training data.

Shake to Leak: Fine-tuning Diffusion Models Can Amplify the Generative Privacy Risk

We propose a new risk to published generative models that finetuning on generated samples can exacerbate the privacy leakage.

DP-OPT: Make Large Language Model Your Privacy-Preserving Prompt Engineer

We make local LLMs to engineer privacy-preserving prompts that are transferrable for cloud models.