9 citations · 13 across the 8 of their papers we have counts for
9 papers
Rethinking Routing Exploration in Modern Mixture-of-Experts
Bingcong Li, Hao Ma, Michael Muehlebach +2
Mixture-of-experts (MoE) models have become an important approach for scaling model parameters. Modern MoE architectures increasingly combine two design choices: fine-grained exper…
SALAAD: Sparse And Low-Rank Adaptation via ADMM for Large Language Model Inference
Hao Ma, Melis Ilayda Bal, Liang Zhang +4
Modern large language models are increasingly deployed under compute and memory constraints, making flexible control of model capacity a central challenge. While sparse and low-ran…
Learning Auxiliary Tasks Improves Reference-Free Hallucination Detection in Open-Domain Long-Form Generation
Chengwei Qin, Wenxuan Zhou, Karthik Abinav Sankararaman +10
Hallucination, the generation of factually incorrect information, remains a significant challenge for large language models (LLMs), especially in open-domain long-form generation.…
Think Smarter not Harder: Adaptive Reasoning with Inference Aware Optimization
Zishun Yu, Tengyu Xu, Di Jin +9
Solving mathematics problems has been an intriguing capability of large language models, and many efforts have been made to improve reasoning by extending reasoning length, such as…
Step-KTO: Optimizing Mathematical Reasoning through Stepwise Binary Feedback
Yen-Ting Lin, Di Jin, Tengyu Xu +11
Large language models (LLMs) have recently demonstrated remarkable success in mathematical reasoning. Despite progress in methods like chain-of-thought prompting and self-consisten…
Beyond Reward Hacking: Causal Rewards for Large Language Model Alignment
Chaoqi Wang, Zhuokai Zhao, Yibo Jiang +8
Recent advances in large language models (LLMs) have demonstrated significant progress in performing complex tasks. While Reinforcement Learning from Human Feedback (RLHF) has been…
Preference Optimization with Multi-Sample Comparisons
Chaoqi Wang, Zhuokai Zhao, Chen Zhu +8
Recent advancements in generative models, particularly large language models (LLMs) and diffusion models, have been driven by extensive pretraining on large datasets followed by po…
The Perfect Blend: Redefining RLHF with Mixture of Judges
Tengyu Xu, Eryk Helenowski, Karthik Abinav Sankararaman +17
Reinforcement learning from human feedback (RLHF) has become the leading approach for fine-tuning large language models (LLM). However, RLHF has limitations in multi-task learning…
Effective Long-Context Scaling of Foundation Models
Wenhan Xiong, Jingyu Liu, Igor Molybog +18
We present a series of long-context LLMs that support effective context windows of up to 32,768 tokens. Our model series are built through continual pretraining from Llama 2 with l…