2 citations · 4 across the 12 of their papers we have counts for
5 papers · 1 filter
Let's reward step by step: Step-Level reward model as the Navigators for Reasoning
Qianli Ma, Haotian Zhou, Tingkai Liu +4
Recent years have seen considerable advancements in multi-step reasoning with Large Language Models (LLMs). The previous studies have elucidated the merits of integrating feedback…
Video-Teller: Enhancing Cross-Modal Generation with Fusion and Decoupling
Haogeng Liu, Qihang Fan, Tingkai Liu +5
This paper proposes Video-Teller, a video-language foundation model that leverages multi-modal fusion and fine-grained modality alignment to significantly enhance the video-to-text…
DavIR: Data Selection via Implicit Reward for Large Language Models
Haotian Zhou, Tingkai Liu, Qianli Ma +5
We introduce DavIR, a model-based data selection method for post-training Large Language Models. DavIR generalizes Reducible Holdout Loss to core-set selection problem of causal la…
DeVAn: Dense Video Annotation for Video-Language Models
Tingkai Liu, Yunzhe Tao, Haogeng Liu +5
We present a novel human annotated dataset for evaluating the ability for visual-language models to generate both short and long descriptions for real-world video clips, termed DeV…
Expedited Training of Visual Conditioned Language Generation via Redundancy Reduction
Yiren Jian, Tingkai Liu, Yunzhe Tao +3
In this paper, we introduce , a streamlined framework designed for the pre-training of visually conditioned language generation models with high computatio…