3 accepted papers
Diffusion Large Language Models (DLLMs) promise fast non-autoregressive inference but suffer a severe quality and speed tradeoff in parallel decoding. This stems from the "combinatorial contradiction" phenomenon, where parallel tokens form semantically inconsistent combinations. We address this by i
Diffusion Large Language Models (DLLMs) have emerged as a compelling alternative to Autoregressive models, designed for fast parallel generation. However, existing DLLMs are plagued by a severe quality-speed trade-off, where faster parallel decoding leads to significant performance degradation. We a…
We propose L2T, an advancement of visual instruction tuning (VIT). While VIT equips Multimodal LLMs (MLLMs) with promising multimodal capabilities, the current design choices for VIT often result in overfitting and shortcut learning, potentially degrading performance. This gap arises from an overemp…