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Unleashing the potential of prompt engineering in large language models: a comprehensive review
B Chen, Z Zhang, N Langrené, S Zhu - arxiv preprint arxiv:2310.14735, 2023 - arxiv.org
This comprehensive review delves into the pivotal role of prompt engineering in unleashing
the capabilities of Large Language Models (LLMs). The development of Artificial Intelligence …
the capabilities of Large Language Models (LLMs). The development of Artificial Intelligence …
Mm-llms: Recent advances in multimodal large language models
In the past year, MultiModal Large Language Models (MM-LLMs) have undergone
substantial advancements, augmenting off-the-shelf LLMs to support MM inputs or outputs …
substantial advancements, augmenting off-the-shelf LLMs to support MM inputs or outputs …
Segment anything
A Kirillov, E Mintun, N Ravi, H Mao… - Proceedings of the …, 2023 - openaccess.thecvf.com
Abstract We introduce the Segment Anything (SA) project: a new task, model, and dataset for
image segmentation. Using our efficient model in a data collection loop, we built the largest …
image segmentation. Using our efficient model in a data collection loop, we built the largest …
Qwen technical report
Large language models (LLMs) have revolutionized the field of artificial intelligence,
enabling natural language processing tasks that were previously thought to be exclusive to …
enabling natural language processing tasks that were previously thought to be exclusive to …
Vmamba: Visual state space model
Designing computationally efficient network architectures remains an ongoing necessity in
computer vision. In this paper, we adapt Mamba, a state-space language model, into …
computer vision. In this paper, we adapt Mamba, a state-space language model, into …
Imagebind: One embedding space to bind them all
R Girdhar, A El-Nouby, Z Liu, M Singh… - Proceedings of the …, 2023 - openaccess.thecvf.com
We present ImageBind, an approach to learn a joint embedding across six different
modalities-images, text, audio, depth, thermal, and IMU data. We show that all combinations …
modalities-images, text, audio, depth, thermal, and IMU data. We show that all combinations …
Mmmu: A massive multi-discipline multimodal understanding and reasoning benchmark for expert agi
We introduce MMMU: a new benchmark designed to evaluate multimodal models on
massive multi-discipline tasks demanding college-level subject knowledge and deliberate …
massive multi-discipline tasks demanding college-level subject knowledge and deliberate …
Sigmoid loss for language image pre-training
X Zhai, B Mustafa, A Kolesnikov… - Proceedings of the …, 2023 - openaccess.thecvf.com
We propose a simple pairwise sigmoid loss for image-text pre-training. Unlike standard
contrastive learning with softmax normalization, the sigmoid loss operates solely on image …
contrastive learning with softmax normalization, the sigmoid loss operates solely on image …
Sharegpt4v: Improving large multi-modal models with better captions
Modality alignment serves as the cornerstone for large multi-modal models (LMMs).
However, the impact of different attributes (eg, data type, quality, and scale) of training data …
However, the impact of different attributes (eg, data type, quality, and scale) of training data …
Llama-adapter: Efficient fine-tuning of language models with zero-init attention
We present LLaMA-Adapter, a lightweight adaption method to efficiently fine-tune LLaMA
into an instruction-following model. Using 52K self-instruct demonstrations, LLaMA-Adapter …
into an instruction-following model. Using 52K self-instruct demonstrations, LLaMA-Adapter …