5 papers
Few-Step Diffusion Language Models via Trajectory Self-Distillation
Tunyu Zhang, Xinxi Zhang, Ligong Han +9
Diffusion large language models (DLLMs) have emerged as powerful generative models with the promise of fast text generation through parallel decoding. However, realizing this poten…
TokUR: Token-Level Uncertainty Estimation for Large Language Model Reasoning
Tunyu Zhang, Haizhou Shi, Yibin Wang +9
While Large Language Models (LLMs) have demonstrated impressive capabilities, their output quality remains inconsistent across various application scenarios, making it difficult to…
Overcoming the Curvature Bottleneck in MeanFlow
Xinxi Zhang, Shiwei Tan, Quang Nguyen +7
MeanFlow offers a promising framework for one-step generative modeling by directly learning a mean-velocity field, bypassing expensive numerical integration. However, we find that…
Dist2ill: Distributional Distillation for One-Pass Uncertainty Estimation in Large Language Models
Yicong Zhao, King Yeung Tsang, Harshil Vejendla +9
Large Language Models (LLMs) often exhibit misalignment between the quality of their generated responses and the confidence estimates they assign to them. Bayesian treatments, such…
Multimodal Needle in a Haystack: Benchmarking Long-Context Capability of Multimodal Large Language Models
Hengyi Wang, Haizhou Shi, Shiwei Tan +6
Multimodal Large Language Models (MLLMs) have shown significant promise in various applications, leading to broad interest from researchers and practitioners alike. However, a comp…