2 citations · 3 across the 4 of their papers we have counts for
8 papers
HiPO: Hybrid Policy Optimization for Dynamic Reasoning in LLMs
Ken Deng, Zizheng Zhan, Wen Xiang +25
Large Language Models (LLMs) increasingly rely on Chain-of-Thought (CoT) reasoning to improve accuracy on complex tasks. However, always generating lengthy reasoning traces is inef…
The Gap Between Principle and Practice of Lossy Image Coding
Haotian Zhang, Dong Liu
Lossy image coding is the art of computing that is principally bounded by the image's rate-distortion function. This bound, though never accurately characterized, has been approach…
Revisit Large-Scale Image-Caption Data in Pre-training Multimodal Foundation Models
Zhengfeng Lai, Vasileios Saveris, Chen Chen +9
Recent advancements in multimodal models highlight the value of rewritten captions for improving performance, yet key challenges remain. For example, while synthetic captions often…
MM1.5: Methods, Analysis & Insights from Multimodal LLM Fine-tuning
Haotian Zhang, Mingfei Gao, Zhe Gan +20
We present MM1.5, a new family of multimodal large language models (MLLMs) designed to enhance capabilities in text-rich image understanding, visual referring and grounding, and mu…
Real or Robotic? Assessing Whether LLMs Accurately Simulate Qualities of Human Responses in Dialogue
Jonathan Ivey, Shivani Kumar, Jiayu Liu +12
Studying and building datasets for dialogue tasks is both expensive and time-consuming due to the need to recruit, train, and collect data from study participants. In response, muc…
Recurrent Inference in Text Editing
Ning Shi, Ziheng Zeng, Haotian Zhang +1
In neural text editing, prevalent sequence-to-sequence based approaches directly map the unedited text either to the edited text or the editing operations, in which the performance…