3 citations · 4 across the 7 of their papers we have counts for
5 papers · 1 filter
Does RLHF Scale? Exploring the Impacts From Data, Model, and Method
Zhenyu Hou, Pengfan Du, Yilin Niu +7
This study explores the scaling properties of Reinforcement Learning from Human Feedback (RLHF) in Large Language Models (LLMs). Although RLHF is considered an important step in po…
VisionReward: Fine-Grained Multi-Dimensional Human Preference Learning for Image and Video Generation
Jiazheng Xu, Yu Huang, Jiale Cheng +19
Visual generative models have achieved remarkable progress in synthesizing photorealistic images and videos, yet aligning their outputs with human preferences across critical dimen…
SPaR: Self-Play with Tree-Search Refinement to Improve Instruction-Following in Large Language Models
Jiale Cheng, Xiao Liu, Cunxiang Wang +7
Instruction-following is a fundamental capability of language models, requiring the model to recognize even the most subtle requirements in the instructions and accurately reflect…
LogicGame: Benchmarking Rule-Based Reasoning Abilities of Large Language Models
Jiayi Gui, Yiming Liu, Jiale Cheng +6
Large Language Models (LLMs) have demonstrated notable capabilities across various tasks, showcasing complex problem-solving abilities. Understanding and executing complex rules, a…
AutoDetect: Towards a Unified Framework for Automated Weakness Detection in Large Language Models
Jiale Cheng, Yida Lu, Xiaotao Gu +6
Although Large Language Models (LLMs) are becoming increasingly powerful, they still exhibit significant but subtle weaknesses, such as mistakes in instruction-following or coding…