563 citations · 1.2k across the 32 of their papers we have counts for
35 papers
The Llama 3 Herd of Models
Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri +556
Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models th…
MINT-1T: Scaling Open-Source Multimodal Data by 10x: A Multimodal Dataset with One Trillion Tokens
Anas Awadalla, Le Xue, Oscar Lo +11
Multimodal interleaved datasets featuring free-form interleaved sequences of images and text are crucial for training frontier large multimodal models (LMMs). Despite the rapid pro…
Enhancing Large Vision Language Models with Self-Training on Image Comprehension
Yihe Deng, Pan Lu, Fan Yin +6
Large vision language models (LVLMs) integrate large language models (LLMs) with pre-trained vision encoders, thereby activating the perception capability of the model to understan…
RAFT: Adapting Language Model to Domain Specific RAG
Tianjun Zhang, Shishir G. Patil, Naman Jain +4
Pretraining Large Language Models (LLMs) on large corpora of textual data is now a standard paradigm. When using these LLMs for many downstream applications, it is common to additi…
Aligning Large Multimodal Models with Factually Augmented RLHF
Zhiqing Sun, Sheng Shen, Shengcao Cao +9
Large Multimodal Models (LMM) are built across modalities and the misalignment between two modalities can result in "hallucination", generating textual outputs that are not grounde…
AgentBench: Evaluating LLMs as Agents
Xiao Liu, Hao Yu, Hanchen Zhang +19
The potential of Large Language Model (LLM) as agents has been widely acknowledged recently. Thus, there is an urgent need to quantitatively \textit{evaluate LLMs as agents} on cha…
SqueezeLLM: Dense-and-Sparse Quantization
Sehoon Kim, Coleman Hooper, Amir Gholami +5
Generative Large Language Models (LLMs) have demonstrated remarkable results for a wide range of tasks. However, deploying these models for inference has been a significant challen…
Mixture-of-Experts Meets Instruction Tuning:A Winning Combination for Large Language Models
Sheng Shen, Le Hou, Yanqi Zhou +17
Sparse Mixture-of-Experts (MoE) is a neural architecture design that can be utilized to add learnable parameters to Large Language Models (LLMs) without increasing inference cost.…
Poisoning Language Models During Instruction Tuning
Alexander Wan, Eric Wallace, Sheng Shen +1
Instruction-tuned LMs such as ChatGPT, FLAN, and InstructGPT are finetuned on datasets that contain user-submitted examples, e.g., FLAN aggregates numerous open-source datasets and…
Scaling Vision-Language Models with Sparse Mixture of Experts
Sheng Shen, Zhewei Yao, Chunyuan Li +3
The field of natural language processing (NLP) has made significant strides in recent years, particularly in the development of large-scale vision-language models (VLMs). These mod…
Multitask Vision-Language Prompt Tuning
Sheng Shen, Shijia Yang, Tianjun Zhang +4
Prompt Tuning, conditioning on task-specific learned prompt vectors, has emerged as a data-efficient and parameter-efficient method for adapting large pretrained vision-language mo…
What Language Model to Train if You Have One Million GPU Hours?
Teven Le Scao, Thomas Wang, Daniel Hesslow +16
The crystallization of modeling methods around the Transformer architecture has been a boon for practitioners. Simple, well-motivated architectural variations can transfer across t…
BLOOM: A 176B-Parameter Open-Access Multilingual Language Model
BigScience Workshop, :, Teven Le Scao +391
Large language models (LLMs) have been shown to be able to perform new tasks based on a few demonstrations or natural language instructions. While these capabilities have led to wi…
Crosslingual Generalization through Multitask Finetuning
Niklas Muennighoff, Thomas Wang, Lintang Sutawika +16
Multitask prompted finetuning (MTF) has been shown to help large language models generalize to new tasks in a zero-shot setting, but so far explorations of MTF have focused on Engl…
ITSRN++: Stronger and Better Implicit Transformer Network for Continuous Screen Content Image Super-Resolution
Sheng Shen, Huanjing Yue, Jingyu Yang +1
Nowadays, online screen sharing and remote cooperation are becoming ubiquitous. However, the screen content may be downsampled and compressed during transmission, while it may be d…
K-LITE: Learning Transferable Visual Models with External Knowledge
Sheng Shen, Chunyuan Li, Xiaowei Hu +11
The new generation of state-of-the-art computer vision systems are trained from natural language supervision, ranging from simple object category names to descriptive captions. Thi…
One Parameter Defense -- Defending against Data Inference Attacks via Differential Privacy
Dayong Ye, Sheng Shen, Tianqing Zhu +2
Machine learning models are vulnerable to data inference attacks, such as membership inference and model inversion attacks. In these types of breaches, an adversary attempts to inf…
Staged Training for Transformer Language Models
Sheng Shen, Pete Walsh, Kurt Keutzer +3
The current standard approach to scaling transformer language models trains each model size from a different random initialization. As an alternative, we consider a staged training…
Implicit Transformer Network for Screen Content Image Continuous Super-Resolution
Jingyu Yang, Sheng Shen, Huanjing Yue +1
Nowadays, there is an explosive growth of screen contents due to the wide application of screen sharing, remote cooperation, and online education. To match the limited terminal ban…
Multitask Prompted Training Enables Zero-Shot Task Generalization
Victor Sanh, Albert Webson, Colin Raffel +38
Large language models have recently been shown to attain reasonable zero-shot generalization on a diverse set of tasks (Brown et al., 2020). It has been hypothesized that this is a…
What's Hidden in a One-layer Randomly Weighted Transformer?
Sheng Shen, Zhewei Yao, Douwe Kiela +2
We demonstrate that, hidden within one-layer randomly weighted neural networks, there exist subnetworks that can achieve impressive performance, without ever modifying the weight i…
How Much Can CLIP Benefit Vision-and-Language Tasks?
Sheng Shen, Liunian Harold Li, Hao Tan +5
Most existing Vision-and-Language (V&L) models rely on pre-trained visual encoders, using a relatively small set of manually-annotated data (as compared to web-crawled data), to pe…
Learned Token Pruning for Transformers
Sehoon Kim, Sheng Shen, David Thorsley +4
Deploying transformer models in practice is challenging due to their inference cost, which scales quadratically with input sequence length. To address this, we present a novel Lear…
LEAP: Learnable Pruning for Transformer-based Models
Zhewei Yao, Xiaoxia Wu, Linjian Ma +4
Pruning is an effective method to reduce the memory footprint and computational cost associated with large natural language processing models. However, current pruning algorithms e…