7 papers
ACDiT: Interpolating Autoregressive Conditional Modeling and Diffusion Transformer
Jinyi Hu, Shengding Hu, Yuxuan Song +6
Autoregressive and diffusion models have achieved remarkable progress in language models and visual generation, respectively. We present ACDiT, a novel Autoregressive blockwise Con…
H-Neurons: On the Existence, Impact, and Origin of Hallucination-Associated Neurons in LLMs
Cheng Gao, Huimin Chen, Chaojun Xiao +3
Large language models (LLMs) frequently generate hallucinations -- plausible but factually incorrect outputs -- undermining their reliability. While prior work has examined halluci…
Large Language Models' Complicit Responses to Illicit Instructions across Socio-Legal Contexts
Xing Wang, Huiyuan Xie, Yiyan Wang +7
Large language models (LLMs) are now deployed at unprecedented scale, assisting millions of users in daily tasks. However, the risk of these models assisting unlawful activities re…
Enhancing Open-Domain Task-Solving Capability of LLMs via Autonomous Tool Integration from GitHub
Bohan Lyu, Xin Cong, Heyang Yu +9
Large Language Models (LLMs) excel in traditional natural language processing tasks but struggle with problems that require complex domain-specific calculations or simulations. Whi…
Rational Decision-Making Agent with Internalized Utility Judgment
Yining Ye, Xin Cong, Shizuo Tian +5
Large language models (LLMs) have demonstrated remarkable advancements and have attracted significant efforts to develop LLMs into agents capable of executing intricate multi-step…
A Multi-Power Law for Loss Curve Prediction Across Learning Rate Schedules
Kairong Luo, Haodong Wen, Shengding Hu +5
Training large models is both resource-intensive and time-consuming, making it crucial to understand the quantitative relationship between model performance and hyperparameters. In…