Summary Efficient Vision-Language Instruction Tuning for LLMs arxiv.org
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Efficient Vision-Language Instruction Tuning for LLMs proposes a solution for optimizing multimodal LLMs with a novel routing algorithm called Mixture-of-Modality Adaptation (MMA), resulting in superior reasoning ability and reduced training time and GPU memory usage.
Key Points
- Efficient Vision-Language Instruction Tuning for LLMs proposes a cost-effective solution called Mixture-of-Modality Adaptation (MMA) that allows Large Language Models (LLMs) to shift between single- and multi-modal instructions without sacrificing natural language understanding abilities.
- The proposed solution, LaVIN, is validated through experiments on multimodal science question answering and multimodal dialogue, demonstrating competitive performance and superior training efficiency compared to existing multimodal LLMs.
- LaVIN is a large vision-language instructed model that predicts the next token using a multimodal input. It is parameter-efficient and lightweight, with a simpler architecture than previous works and a visual adapter to transform visual features.
- The Mixed-Modality Adversarial (MMA) training scheme optimizes the entire model end-to-end without requiring additional VL pre-training, keeping the number of optimized parameters at a small scale, reducing training time and storage cost.
- LaVIN achieves strong results on ScienceQA, outperforming existing few-shot LLMs by using a larger LLM, MM-Adapter, stronger image encoder, joint optimization, and vision modality. It includes adapters for different tasks such as translation, math, coding, image captioning, and visual question answering.
Summaries
232 word summary
Efficient Vision-Language Instruction Tuning for LLMs proposes a solution for vision-language instruction tuning called Mixture-of-Modality Adaptation (MMA) using a novel routing algorithm to optimize the multimodal LLM via a small number of parameters, reducing training costs. LaVIN, a visual language model for instruction-following tasks, demonstrates superior reasoning ability compared to existing multimodal LLMs and achieves overall better responses across multiple tasks while reducing training time and GPU memory usage by up to 80%. The paper compares LaVIN with existing multimodal LLMs in multi-turn conversations and shows that LaVIN has a higher GPT4 score among all compared models, suggesting the significance of MMA in adapting LLMs to multi-modal tasks. The paper also lists various studies that have improved vision-language understanding and generation, including prefix-tuning, bootstrapping language-image pre-training, and grounding language models to images. The paper mentions various libraries such as FairScale and Vicuna. Efficient Vision-Language Instruction Tuning for LLMs is a document that focuses on language understanding with advanced large language models. It references various sources related to machine learning, including papers on transformer models, language models, visual models, and mathematical reasoning tasks. The document discusses enhancing vision-language models, conditional prompt learning for chain-of-thought reasoning in language models, multimodal action, and efficient fine-tuning of language models with zero-init attention. Additionally, the document discusses benchmarking and analysis platforms for natural language understanding, open and efficient foundation language models, and solving AI tasks with chatgpt.
513 word summary
Efficient Vision-Language Instruction Tuning for LLMs is a document that focuses on language understanding with advanced large language models. It references various sources related to machine learning, including papers on transformer models, language models, visual models, and mathematical reasoning tasks. The document discusses enhancing vision-language models, conditional prompt learning for chain-of-thought reasoning in language models, multimodal action, and efficient fine-tuning of language models with zero-init attention. Additionally, the document discusses benchmarking and analysis platforms for natural language understanding, open and efficient foundation language models, and solving AI tasks with chatgpt.
The Efficient Vision-Language Instruction Tuning for LLMs paper proposes a solution for vision-language instruction tuning called Mixture-of-Modality Adaptation (MMA) which uses a novel routing algorithm to optimize the multimodal LLM via a small number of parameters, reducing training costs. LaVIN, a visual language model for instruction-following tasks, demonstrates superior reasoning ability compared to existing multimodal LLMs and achieves overall better responses across multiple tasks while reducing training time and GPU memory usage by up to 80%. The paper compares LaVIN with existing multimodal LLMs in multi-turn conversations and shows that LaVIN has a higher GPT4 score among all compared models, suggesting the significance of MMA in adapting LLMs to multi-modal tasks.
The paper also lists various studies that have improved vision-language understanding and generation, including prefix-tuning, bootstrapping language-image pre-training, and grounding language models to images. The paper mentions various libraries such as FairScale and Vicuna. LaVIN is a new method for efficient vision-language instruction tuning using Mixture-of-Modality Adaptation (MMA) that can adapt to different modalities and preserve NLP capabilities of Large Language Models (LLMs). The MMA training scheme optimizes the entire model end-to-end without requiring additional VL pre-training, reducing training time and storage cost. LaVIN achieves strong results on ScienceQA and large-scale datasets such as Alphaca-52k and LLaVA-158k. It outperforms existing approaches with significantly lower training costs and achieves comparable performance to LLaVA. LaVIN includes adapters for different tasks such as translation, math, coding, image captioning, and visual question answering. The model also features a multimodal reasoning adapter that improves accuracy for both image-based and text-based questions. The paper releases the source code and pre-trained checkpoints associated with LaVIN. Experimental results show that LaVIN is superior in efficiency and performance compared to existing multimodal chatbots. The article also discusses the effectiveness of different LLMs, including Flamingo and InstructGPT, and the importance of instruction tuning for improving their performance on downstream tasks. Efficient Vision-Language Instruction Tuning for LLMs presents MMA, a cost-effective solution to allow Large Language Models (LLMs) to switch between single- and multi-modal instructions without sacrificing natural language understanding abilities. MMA uses lightweight adapters to enable joint optimization of image and language parameters. The proposed solution, LaVIN, is validated through experiments on multimodal science question answering and multimodal dialogue, demonstrating competitive performance and superior training efficiency compared to existing multimodal LLMs. MMA is an end-to-end optimization scheme that is cheap in training and efficient in automatic shift between text-only and image-text instructions. Modular training of LLMs is preferred over the ensemble of LLM and vision models due to computation and storage overhead.
785 word summary
Efficient Vision-Language Instruction Tuning for LLMs proposes a cost-effective solution called Mixture-of-Modality Adaptation (MMA) that allows Large Language Models (LLMs) to shift between single- and multi-modal instructions without sacrificing natural language understanding abilities. MMA uses lightweight adapters to bridge the gap between LLMs and vision tasks, enabling joint optimization of image and language parameters. The proposed solution, LaVIN, is validated through experiments on multimodal science question answering and multimodal dialogue, demonstrating competitive performance and superior training efficiency compared to existing multimodal LLMs. Existing solutions for vision-language learning are expensive and require large-scale pre-training, but MMA is an end-to-end optimization scheme that is cheap in training and efficient in automatic shift between text-only and image-text instructions. Modular training of LLMs is preferred over the ensemble of LLM and vision models due to computation and storage overhead. The paper proposes a new method called LaVIN for efficient vision-language instruction tuning using Mixture-of-Modality Adaptation (MMA) that can adapt to different modalities and preserve NLP capabilities of Large Language Models (LLMs). LaVIN can accurately execute various types of human instructions and achieve cheap and quick adaptations on VL tasks without requiring another large-scale pre-training. MMA is proposed as an end-to-end optimization regime that connects the image encoder and LLM with lightweight adapters to dynamically choose the suitable adaptation path for inputs of different modalities. The paper releases the source code and pre-trained checkpoints associated with LaVIN. Experimental results show that LaVIN is superior in efficiency and performance compared to existing multimodal chatbots. The paper validates LaVIN through quantitative experiments on ScienceQA and by applying MMA to a recently proposed LLM called LLaMA. The article also discusses the effectiveness of different LLMs, including Flamingo and InstructGPT, and the importance of instruction tuning for improving their performance on downstream tasks. LaVIN is a large vision-language instructed model that predicts the next token using a multimodal input. It is parameter-efficient and lightweight, with a simpler architecture than previous works and a visual adapter to transform visual features. The Mixed-Modality Adversarial (MMA) training scheme optimizes the entire model end-to-end without requiring additional VL pre-training, keeping the number of optimized parameters at a small scale, reducing training time and storage cost. LaVIN achieves strong results on ScienceQA, outperforming existing few-shot LLMs by using a larger LLM, MM-Adapter, stronger image encoder, joint optimization, and vision modality. It is evaluated on large-scale datasets such as Alphaca-52k and LLaVA-158k, with LaVIN-13B achieving the best results among the tested models. The study compares LaVIN with existing LLMs and conducts ablation studies to confirm the effectiveness of the proposed designs. LaVIN outperforms existing approaches with significantly lower training costs and achieves comparable performance to LLaVA. It includes adapters for different tasks such as translation, math, coding, image captioning, and visual question answering. The model also features a multimodal reasoning adapter that improves accuracy for both image-based and text-based questions. The Efficient Vision-Language Instruction Tuning for LLMs paper proposes a solution for vision-language instruction tuning called Mixture-of-Modality Adaptation (MMA) which uses a novel routing algorithm to optimize the multimodal LLM via a small number of parameters, reducing training costs. LaVIN, a visual language model for instruction-following tasks, demonstrates superior reasoning ability compared to existing multimodal LLMs and achieves overall better responses across multiple tasks while reducing training time and GPU memory usage by up to 80%. In multi-turn conversations, LaVIN outperforms other LLMs in providing high-quality responses. However, LaVIN has limitations in generating incorrect or fabricate responses and cannot identify extremely fine-grained visual content. The paper compares LaVIN with existing multimodal LLMs in multi-turn conversations and shows that LaVIN has a higher GPT4 score among all compared models, suggesting the significance of MMA in adapting LLMs to multi-modal tasks. The paper also lists various studies that have improved vision-language understanding and generation, including prefix-tuning, bootstrapping language-image pre-training, and grounding language models to images. The paper mentions various libraries such as FairScale and Vicuna. Efficient Vision-Language Instruction Tuning for LLMs is a document that focuses on language understanding with advanced large language models. It references various sources related to machine learning, including papers on transformer models, language models, visual models, and mathematical reasoning tasks. The papers discussed in the document explore the use of prompts for fine-tuning models, unsupervised multitask learning, and efficient visual adaptation via structural re-parameterization. The document also mentions a technical report on GPT-4 and a suite of fundamental mathematical reasoning tasks called Numglue. Key points discussed in the document include enhancing vision-language models, conditional prompt learning for chain-of-thought reasoning in language models, multimodal action, and efficient fine-tuning of language models with zero-init attention. Additionally, the document discusses benchmarking and analysis platforms for natural language understanding, open and efficient foundation language models, and solving AI tasks with chatgpt.
1835 word summary
Efficient Vision-Language Instruction Tuning for LLMs is a document that includes several arXiv preprints and proceedings that focus on language understanding with advanced large language models. Some of the key points discussed in the document include enhancing vision-language models, conditional prompt learning for chain-of-thought reasoning in language models, multimodal action, and efficient fine-tuning of language models with zero-init attention. Additionally, the document discusses benchmarking and analysis platforms for natural language understanding, open and efficient foundation language models, and solving AI tasks with chatgpt. Efficient Vision-Language Instruction Tuning for LLMs is a document that references various sources related to machine learning. The sources include papers on transformer models, language models, visual models, and mathematical reasoning tasks. Some of the papers discuss the use of prompts for fine-tuning models, while others explore unsupervised multitask learning. One paper presents a new dataset for open book question answering, and another proposes a method for efficient visual adaptation via structural re-parameterization. The document also mentions a technical report on GPT-4 and a suite of fundamental mathematical reasoning tasks called Numglue. Finally, there is a paper on decoupled weight decay regularization and another on visual instruction tuning. Efficient Vision-Language Instruction Tuning for LLMs is a research paper that lists various studies that have improved vision-language understanding and generation. Some of these studies include prefix-tuning, bootstrapping language-image pre-training, and grounding language models to images. Other studies mentioned in the paper are parameter-efficient transfer learning for NLP, scaling instruction-finetuned language models, and adapting vision transformers for scalable visual recognition. Additionally, the paper mentions various libraries such as FairScale, which is a general-purpose modular PyTorch library for high-performance and large-scale training, and Vicuna, an open-source chatbot that impresses with GPT-4 with 90% chatGPT quality. The paper proposes a solution for vision-language instruction tuning called Mixture-of-Modality Adaptation (MMA) which uses a novel routing algorithm to optimize the multimodal LLM via a small number of parameters, reducing training costs. MMA is capable of shifting reasoning paths for single- and multi-modal instructions, making it an affordable end-to-end optimization ability. LaVIN, a visual language model for instruction-following tasks, demonstrates superior reasoning ability compared to existing multimodal LLMs. However, LaVIN has limitations in generating incorrect or fabricate responses and cannot identify extremely fine-grained visual content. The paper compares LaVIN with existing multimodal LLMs in multi-turn conversations and shows that LaVIN has a higher GPT4 score among all compared models, suggesting the significance of MMA in adapting LLMs to multi-modal tasks. LaVIN demonstrates superior visual reasoning ability in executing single- and multi-modal instructions, including complex scenes and math problems. It also presents clear and concise coding behavior with accurate output. Compared to existing LLMs, LaVIN achieves overall better responses across multiple tasks and reduces training time and GPU memory usage by up to 80%. In multi-turn conversations, LaVIN outperforms other LLMs in providing high-quality responses. The image shows a man holding a baby and petting a brown horse, with no visible signs of rain or stormy conditions. The baby is wearing a white onesie and reaching out to touch the horse's nose, creating a moment of connection and curiosity for the child. The man and baby are both standing under an overhang, which provides incomplete protection from the rain. If it starts raining, the man will get wet as the overhang will not provide complete protection. The weather in the image appears to be sunny, suggesting that the location might be a farm or rural area where sunny conditions are more common.
In the Efficient Vision-Language Instruction Tuning for LLMs document, the authors compare the training expenditures of LaVIN, LLaVA, and BLIP2, and evaluate the effectiveness of MM-Adapter in adapting multimodal LLM. They find that text-only and text-image instruction inputs have different requirements for their adaptations, and MM-Adapter effectively decouples the inference of different modalities into two sets of adapters. The routing weights of LaVIN for text-only and text-image instruction inputs are very sharp, suggesting that the model is very confident in its decision. The performance of LaVIN is further improved by +1.09, validating the significance of MMA in adapting multimodal LLM. The document presents LaVIN, a language and vision transformer model that dynamically executes different inference paths based on input modality. It includes adapters for different tasks such as translation, math, coding, image captioning, and visual question answering. The model also features a multimodal reasoning adapter that improves accuracy for both image-based and text-based questions. The performance of LaVIN is significantly improved by using a better image encoder. With the help of multimodal tuning, LaVIN surpasses other parameter-efficient methods in terms of accuracy. The results are evaluated on 8 A100 GPUs. Efficient Vision-Language Instruction Tuning for LLMs is a study on improving the performance of multimodal language models (LLMs) on ScienceQA through joint optimization and modality adaptation. The study compares the proposed LaVIN model with existing LLMs, including LLaVA and LLaMA-Adapter, and conducts ablation studies to confirm the effectiveness of the proposed designs. LaVIN outperforms existing approaches with significantly lower training costs and achieves comparable performance to LLaVA. The joint optimization of image encoder and LLM, as well as the mixture-of-modality training, greatly contribute to the final performance. When scaled up to 13B, LaVIN can obtain more significant performance gains. The study also highlights the importance of considering the modality gap in input instructions and the learning of visual content in LLMs. LaVIN is an end-to-end multimodal LLM that achieves strong results on ScienceQA. LaVIN is compared to state-of-the-art methods and outperforms existing few-shot LLMs. The model uses a larger LLM, MM-Adapter, stronger image encoder, joint optimization, and vision modality to achieve these results. The implementation details include using ViT-L/14 as the image encoder and a cosine decay learning rate schedule. The model is evaluated on large-scale datasets such as Alphaca-52k and LLaVA-158k. LaVIN-13B achieves the best results among the tested models, while LLaMA-Adapter achieves the best results among models with fewer parameters. The article presents a new large vision-language instructed model called LaVIN, which is parameter-efficient and lightweight. It uses a multimodal input to predict the next token step by step. The architecture of LaVIN is much simpler than previous works and includes a visual adapter to transform visual features. The training scheme uses a novel method called Mixed-Modality Adversarial (MMA), which optimizes the entire model end-to-end without requiring additional VL pre-training. The number of optimized parameters is kept at a small scale, reducing training time and storage cost. The overall training objective is defined by constructing a mini training batch randomly sampled from text-only and text-image instructions. The ground-truth response and objective loss function are denoted by R and L, respectively. The Efficient Vision-Language Instruction Tuning for LLMs document proposes a learning regime for the vision-language adaptation of LLMs. The proposed method involves using Mixture-of-Modality Adapters and Mixture-of-Modality Training to optimize multimodal LLMs in an end-to-end manner. The Mixture-of-Modality Adapter can be used as the unimodal adapter to improve the adaptation ability, and the Mixture-of-Modality Training targets to freeze the large image encoder and LLM, and only fine-tune the inserted adapters. The process selects the best adaptation path according to the modalities of input instructions, and MMA can dynamically adapt to the input features. During instruction tuning, LaVIN is optimized by Mixture of Modality Training. The proposed method is efficient and cheap in training time and storage. The article discusses efficient tuning methods for multimodal language and vision models (LLMs). Most LLMs require expensive training costs, but recent modular training models have provided more efficient alternatives. The paper proposes a new method called MMA, which dynamically adjusts the adaptations of different modalities to improve adaptation capabilities and inference speed. This method is compared to other PETL methods and AdaMix, which are also aimed at reducing training and storage overhead of LLMs. The article also discusses the effectiveness of different LLMs, including Flamingo and InstructGPT, and the importance of instruction tuning for improving their performance on downstream tasks. The paper proposes a new solution for efficient vision-language instruction tuning called LaVIN, which uses Mixture-of-Modality Adaptation (MMA) to adapt to different modalities and preserve the NLP capabilities of Large Language Models (LLMs). The paper releases the source code and pre-trained checkpoints associated with LaVIN. Experimental results show that LaVIN is superior in efficiency and performance compared to existing multimodal chatbots. LaVIN can accurately execute various types of human instructions, such as coding, math, and image captioning. The paper validates LaVIN through quantitative experiments on ScienceQA and by applying MMA to a recently proposed LLM called LLaMA. MMA reduces training time and storage costs while achieving on-par performance with advanced multimodal LLMs. LaVIN can achieve cheap and quick adaptations on VL tasks without the requirement of another large-scale pre-training. The paper proposes MMA as an end-to-end optimization regime that connects the image encoder and LLM with lightweight adapters, which can dynamically choose the suitable adaptation path for the inputs of different modalities. Existing multimodal LLMs do not support text-only instructions, which undermines the NLP capabilities of LLMs due to drastic changes in their parameter spaces and increases training time and intermediate storage overhead. Efficient Vision-Language Instruction Tuning for LLMs is a preprint document that discusses different multimodal adaptation schemes for LLMs. The required VL pre-training is still expensive for a quick adaptation of cross-modal alignment. The joint optimization of LLMs and vision models still exhibits significant redundancy in terms of computation and parameters, leading to excessive memory footprints. Existing multimodal solutions for LLMs can be roughly divided into two main categories, i.e., the expert system and the modular training ones, respectively. LLMs usually serve as a manager to interpret different natural language instructions, and then call the corresponding vision models to handle the input image. The proposed Mixture-of-Modality Adaptation (MMA) is an end-to-end optimization scheme, which is cheap in training and superior in the automatic shift between text-only and image-text instructions. The modular training of LLMs is preferred because the ensemble of LLM and vision models is expensive in terms of computation and storage overhead. Large language models (LLMs) have been continuously improving in their natural language processing (NLP) abilities, and the introduction of instruction tuning has enabled LLMs to engage in human-like conversations and handle various NLP tasks. However, existing solutions for vision-language (VL) learning are prohibitively expensive and require large-scale pre-training before VL instruction tuning. To address this, the authors propose a novel and affordable solution called Mixture-of-Modality Adaptation (MMA) that enables LLMs to achieve an automatic shift between single- and multi-modal instructions without compromising their natural language understanding abilities. MMA adopts lightweight modules called adapters to bridge the gap between LLMs and VL tasks, which also enables the joint optimization of image and language parameters. The authors validate MMA and their solution, LaVIN, through extensive experiments under two setups: multimodal science question answering and multimodal dialogue. LaVIN demonstrates competitive performance and superior training efficiency compared to existing multimodal LLMs, confirming its great potential as a general-purpose chatbot. The actual expenditure of LaVIN is extremely cheap, making it an effective solution for efficient vision-language instruction tuning for LLMs.