7 papers
When 2D Tasks Meet 1D Serialization: On Serialization Friction in Structured Tasks
Chung-Hsiang Lo, Lu Li, Diji Yang +4
In the LLM era, many symbolic and structured problems are presented to models through 1D text serialization. Yet some such problems are natively two-dimensional: their relevant rel…
Culture-Aware Humorous Captioning: Multimodal Humor Generation across Cultural Contexts
Run Xu, Lu Li, Rongzhao Zhang +1
Recent multimodal large language models have shown promising ability in generating humorous captions for images, yet they still lack stable control over explicit cultural context,…
Large Language Models Explore by Latent Distilling
Yuanhao Zeng, Ao Lu, Lufei Li +3
Generating diverse responses is crucial for test-time scaling of large language models (LLMs), yet standard stochastic sampling mostly yields surface-level lexical variation, limit…
Scaling Latent Reasoning via Looped Language Models
Rui-Jie Zhu, Zixuan Wang, Kai Hua +30
Modern LLMs are trained to "think" primarily via explicit text generation, such as chain-of-thought (CoT), which defers reasoning to post-training and under-leverages pre-training…
STRICT: Stress Test of Rendering Images Containing Text
Tianyu Zhang, Xinyu Wang, Lu Li +5
While diffusion models have revolutionized text-to-image generation with their ability to synthesize realistic and diverse scenes, they continue to struggle to generate consistent…
MAP: Low-compute Model Merging with Amortized Pareto Fronts via Quadratic Approximation
Lu Li, Tianyu Zhang, Zhiqi Bu +7
Model merging has emerged as an effective approach to combine multiple single-task models into a multitask model. This process typically involves computing a weighted average of th…