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SEAL: Steerable Reasoning Calibration of Large Language Models for Free

A training-free approach that calibrates chain-of-thought reasoning in LLMs, improving accuracy while reducing computational overhead.

MedHallu: A Comprehensive Benchmark for Detecting Medical Hallucinations in Large Language Models

Benchmark for medical hallucination by LLMs.

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.