1 citations · 1 across the 7 of their papers we have counts for
8 papers
Scheduling Thoughts: Learning the Order of Thought in Diffusion Language Models
Jiawei Xu, Minghui Liu, Aakriti Agrawal +2
Masked diffusion language models decode by iteratively unmasking tokens, where the unmasking order defines an "order of thought" that strongly influences generation quality yet is…
When Human Preferences Flip: An Instance-Dependent Robust Loss for RLHF
Yifan Xu, Xichen Ye, Yifan Chen +1
Quality of datasets plays an important role in large language model (LLM) alignment. In collecting human feedback, however, preference flipping is ubiquitous and causes corruption…
Optimizing LVLMs with On-Policy Data for Effective Hallucination Mitigation
Chengzhi Yu, Yifan Xu, Yifan Chen +1
Recently, large vision-language models (LVLMs) have risen to be a promising approach for multimodal tasks. However, principled hallucination mitigation remains a critical challenge…
Reasoning Like an Economist: Post-Training on Economic Problems Induces Strategic Generalization in LLMs
Yufa Zhou, Shaobo Wang, Xingyu Dong +7
Directly training Large Language Models (LLMs) for Multi-Agent Systems (MAS) remains challenging due to intricate reward modeling, dynamic agent interactions, and demanding general…
SHARP: Accelerating Language Model Inference by SHaring Adjacent layers with Recovery Parameters
Yiping Wang, Hanxian Huang, Yifang Chen +3
While Large language models (LLMs) have advanced natural language processing tasks, their growing computational and memory demands make deployment on resource-constrained devices l…
LeetDecoding: A PyTorch Library for Exponentially Decaying Causal Linear Attention with CUDA Implementations
Jiaping Wang, Simiao Zhang, Qiao-Chu He +1
The machine learning and data science community has made significant while dispersive progress in accelerating transformer-based large language models (LLMs), and one promising app…
Rethinking Data Synthesis: A Teacher Model Training Recipe with Interpretation
Yifang Chen, David Zhu, Simon Du +2
Recent advances in large language model (LLM) training have highlighted the need for diverse, high-quality instruction data. Recently, many works are exploring synthetic data gener…
Unraveling and Mitigating Retriever Inconsistencies in Retrieval-Augmented Large Language Models
Mingda Li, Xinyu Li, Yifan Chen +2
Although Retrieval-Augmented Large Language Models (RALMs) demonstrate their superiority in terms of factuality, they do not consistently outperform the original retrieval-free Lan…