4 citations · 11 across the 12 of their papers we have counts for
12 papers
CodePercept: Code-Grounded Visual STEM Perception for MLLMs
Tongkun Guan, Zhibo Yang, Jianqiang Wan +10
When MLLMs fail at Science, Technology, Engineering, and Mathematics (STEM) visual reasoning, a fundamental question arises: is it due to perceptual deficiencies or reasoning limit…
Error-Aware Knowledge Distillation via Targeted Revision for Customer-Service Summarization
Hee-Jin Lee, Zhen Guo, Luchao Jin +1
We introduce an Analyze-Revise-Finetune (ARF) pipeline that enables smaller open-source language models (LLMs) to surpass substantially larger proprietary models in customer servic…
Synthetic Data RL: Task Definition Is All You Need
Yiduo Guo, Zhen Guo, Chuanwei Huang +5
Reinforcement learning (RL) is a powerful way to adapt foundation models to specialized tasks, but its reliance on large-scale human-labeled data limits broad adoption. We introduc…
A Token-level Text Image Foundation Model for Document Understanding
Tongkun Guan, Zining Wang, Pei Fu +9
In recent years, general visual foundation models (VFMs) have witnessed increasing adoption, particularly as image encoders for popular multi-modal large language models (MLLMs). H…
Scaling Law Hypothesis for Multimodal Model
Qingyun Sun, Zhen Guo, PIN AI Team
We propose a scaling law hypothesis for multimodal models processing text, audio, images, and video within a shared token and embedding space. Our framework predicts model performa…
Octo-planner: On-device Language Model for Planner-Action Agents
Wei Chen, Zhiyuan Li, Zhen Guo +1
AI agents have become increasingly significant in various domains, enabling autonomous decision-making and problem-solving. To function effectively, these agents require a planning…
More Compute Is What You Need
Zhen Guo
Large language model pre-training has become increasingly expensive, with most practitioners relying on scaling laws to allocate compute budgets for model size and training tokens,…
JetMoE: Reaching Llama2 Performance with 0.1M Dollars
Yikang Shen, Zhen Guo, Tianle Cai +1
Large Language Models (LLMs) have achieved remarkable results, but their increasing resource demand has become a major obstacle to the development of powerful and accessible super-…
Diversity Measurement and Subset Selection for Instruction Tuning Datasets
Peiqi Wang, Yikang Shen, Zhen Guo +4
We aim to select data subsets for the fine-tuning of large language models to more effectively follow instructions. Prior work has emphasized the importance of diversity in dataset…
API Pack: A Massive Multi-Programming Language Dataset for API Call Generation
Zhen Guo, Adriana Meza Soria, Wei Sun +2
We introduce API Pack, a massive multi-programming language dataset containing over one million instruction-API calls for improving the API call generation capabilities of large la…
AuthentiGPT: Detecting Machine-Generated Text via Black-Box Language Models Denoising
Zhen Guo, Shangdi Yu
Large language models (LLMs) have opened up enormous opportunities while simultaneously posing ethical dilemmas. One of the major concerns is their ability to create text that clos…
Continuous Training and Fine-tuning for Domain-Specific Language Models in Medical Question Answering
Zhen Guo, Yining Hua
Large language models exhibit promising general capabilities but often lack specialized knowledge for domain-specific tasks. Developing domain experts from a base model enables a r…