6 papers · 1 filter
Extended Inductive Reasoning for Personalized Preference Inference from Behavioral Signals
Jia-Nan Li, Jian Guan, Wei Wu +1
Large language models (LLMs) have demonstrated significant success in complex reasoning tasks such as math and coding. In contrast to these tasks where deductive reasoning predomin…
Scaling Video-Language Models to 10K Frames via Hierarchical Differential Distillation
Chuanqi Cheng, Jian Guan, Wei Wu +1
Long-form video processing fundamentally challenges vision-language models (VLMs) due to the high computational costs of handling extended temporal sequences. Existing token prunin…
From 1,000,000 Users to Every User: Scaling Up Personalized Preference for User-level Alignment
Jia-Nan Li, Jian Guan, Songhao Wu +2
Large language models (LLMs) have traditionally been aligned through one-size-fits-all approaches that assume uniform human preferences, fundamentally overlooking the diversity in…
2D-TPE: Two-Dimensional Positional Encoding Enhances Table Understanding for Large Language Models
Jia-Nan Li, Jian Guan, Wei Wu +2
Tables are ubiquitous across various domains for concisely representing structured information. Empowering large language models (LLMs) to reason over tabular data represents an ac…
Mixture-of-Modules: Reinventing Transformers as Dynamic Assemblies of Modules
Zhuocheng Gong, Ang Lv, Jian Guan +6
Is it always necessary to compute tokens from shallow to deep layers in Transformers? The continued success of vanilla Transformers and their variants suggests an undoubted "yes".…
From the Least to the Most: Building a Plug-and-Play Visual Reasoner via Data Synthesis
Chuanqi Cheng, Jian Guan, Wei Wu +1
We explore multi-step reasoning in vision-language models (VLMs). The problem is challenging, as reasoning data consisting of multiple steps of visual and language processing are b…