4 papers
Localize-Then-Decide Guarantees for LLM Judgments
Xinyu Li, Yi Zhou, Guanqun Cao +3
Large language models (LLMs) are increasingly used as evaluators to assess output quality and preference alignment, yet providing reliable guarantees of agreement with human judgme…
GRAPE: Guided Parameter-Space Evolution for Compact Adversarial Robustness
Zhiyuan Ye, Xiangyu Zhou, Ji Qi +2
Adversarial Training (AT) improves neural network robustness, but most methods train a fixed parameter space from the start. This paper asks whether the order in which parameters b…
RLDBF: Enhancing LLMs Via Reinforcement Learning With DataBase FeedBack
Weichen Dai, Zijie Dai, Zhijie Huang +6
While current large language models (LLMs) demonstrate remarkable linguistic capabilities through training on massive unstructured text corpora, they remain inadequate in leveragin…
KALE-LM-Chem: Vision and Practice Toward an AI Brain for Chemistry
Weichen Dai, Yezeng Chen, Zijie Dai +9
Recent advancements in large language models (LLMs) have demonstrated strong potential for enabling domain-specific intelligence. In this work, we present our vision for building a…