32 citations · 88 across the 34 of their papers we have counts for
10 papers · 1 filter
G-LLaVA: Solving Geometric Problem with Multi-Modal Large Language Model
Jiahui Gao, Renjie Pi, Jipeng Zhang +8
Large language models (LLMs) have shown remarkable proficiency in human-level reasoning and generation capabilities, which encourages extensive research on their application in mat…
PerceptionGPT: Effectively Fusing Visual Perception into LLM
Renjie Pi, Lewei Yao, Jiahui Gao +2
The integration of visual inputs with large language models (LLMs) has led to remarkable advancements in multi-modal capabilities, giving rise to visual large language models (VLLM…
Plum: Prompt Learning using Metaheuristic
Rui Pan, Shuo Xing, Shizhe Diao +6
Since the emergence of large language models, prompt learning has become a popular method for optimizing and customizing these models. Special prompts, such as Chain-of-Thought, ha…
MarineGPT: Unlocking Secrets of Ocean to the Public
Ziqiang Zheng, Jipeng Zhang, Tuan-Anh Vu +3
Large language models (LLMs), such as ChatGPT/GPT-4, have proven to be powerful tools in promoting the user experience as an AI assistant. The continuous works are proposing multi-…
Non-Autoregressive Sentence Ordering
Yi Bin, Wenhao Shi, Bin Ji +3
Existing sentence ordering approaches generally employ encoder-decoder frameworks with the pointer net to recover the coherence by recurrently predicting each sentence step-by-step…
Mitigating the Alignment Tax of RLHF
Yong Lin, Hangyu Lin, Wei Xiong +14
LLMs acquire a wide range of abilities during pre-training, but aligning LLMs under Reinforcement Learning with Human Feedback (RLHF) can lead to forgetting pretrained abilities, w…
LMFlow: An Extensible Toolkit for Finetuning and Inference of Large Foundation Models
Shizhe Diao, Rui Pan, Hanze Dong +4
Foundation models have demonstrated a great ability to achieve general human-level intelligence far beyond traditional approaches. As the technique keeps attracting attention from…
DetGPT: Detect What You Need via Reasoning
Renjie Pi, Jiahui Gao, Shizhe Diao +8
In recent years, the field of computer vision has seen significant advancements thanks to the development of large language models (LLMs). These models have enabled more effective…
RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment
Hanze Dong, Wei Xiong, Deepanshu Goyal +7
Generative foundation models are susceptible to implicit biases that can arise from extensive unsupervised training data. Such biases can produce suboptimal samples, skewed outcome…
Toward Building General Foundation Models for Language, Vision, and Vision-Language Understanding Tasks
Xinsong Zhang, Yan Zeng, Jipeng Zhang +1
Foundation models or pre-trained models have substantially improved the performance of various language, vision, and vision-language understanding tasks. However, existing foundati…