6 papers
LoPA: Scaling dLLM Inference via Lookahead Parallel Decoding
Chenkai Xu, Yijie Jin, Jiajun Li +8
Diffusion Large Language Models (dLLMs) have demonstrated significant potential for high-speed inference. However, current confidence-driven decoding strategies are constrained by…
SparseRM: A Lightweight Preference Modeling with Sparse Autoencoder
Dengcan Liu, Jiahao Li, Zheren Fu +4
Reward models (RMs) are a core component in the post-training of large language models (LLMs), serving as proxies for human preference evaluation and guiding model alignment. Howev…
APRIL: Active Partial Rollouts in Reinforcement Learning to Tame Long-tail Generation
Yuzhen Zhou, Jiajun Li, Yusheng Su +15
Reinforcement learning (RL) has become a cornerstone in advancing large-scale pre-trained language models (LLMs). Successive generations, including GPT-o series, DeepSeek-R1, Kimi-…
Video-LevelGauge: Investigating Contextual Positional Bias in Large Video Language Models
Hou Xia, Zheren Fu, Fangcan Ling +4
Large video language models (LVLMs) have made notable progress in video understanding, spurring the development of corresponding evaluation benchmarks. However, existing benchmarks…
Context-Aware Probabilistic Modeling with LLM for Multimodal Time Series Forecasting
Yueyang Yao, Jiajun Li, Xingyuan Dai +4
Time series forecasting is important for applications spanning energy markets, climate analysis, and traffic management. However, existing methods struggle to effectively integrate…
Human-in-the-Loop Generation of Adversarial Texts: A Case Study on Tibetan Script
Xi Cao, Yuan Sun, Jiajun Li +3
DNN-based language models excel across various NLP tasks but remain highly vulnerable to textual adversarial attacks. While adversarial text generation is crucial for NLP security,…