Multifaceted models strive to integrate data from various sources, including written language, images, and videos, to perform various functions. These models have shown considerable potential for understanding and generating content that merges visual and textual data.
A crucial component of multifaceted models is instruction tuning, which involves tuning the model based on natural language directives. This allows the model to better capture user intent and generate accurate and relevant responses. Instruction wrapping has been effectively employed in Long Language Models (LLMs) such as GPT-2 and GPT-3, allowing them to follow instructions to perform real-world tasks.
Existing approaches in multimodal models can be categorized into systems design and end-to-end trainable model perspectives. The system design perspective connects different models using a dispatch scheduler like ChatGPT, but it lacks training flexibility and can be expensive. The end-to-end trainable models perspective integrates models from other modalities, but may have high training costs or limited flexibility. Previous instruction fitting data sets on multimodal models lack in-context examples. Recently, a new approach proposed by a Singaporean research team introduces instruction-in-context setting and builds datasets with contextual examples to fill this gap.
The main contributions of this work include:
- The introduction of the MIMIC-IT dataset for instruction fitting in multimodal models.
- The development of the Otter model with better instruction-following abilities and learning in context.
- Optimizing the OpenFlamingo implementation for ease of accessibility.
These contributions provide researchers with a valuable data set, an improved model, and a more user-friendly framework to advance multimodal research.
Specifically, the authors present the MIMIC-IT dataset, which aims to improve OpenFlamingo’s instruction comprehension capabilities while preserving its ability to learn in context. The dataset consists of image-text pairs with contextual relationships, whereas OpenFlamingo aims to generate text for a queried image-text pair based on in-context examples. The MIMIC-IT dataset is presented to improve understanding of OpenFlamingo instructions while keeping learning in context. It includes image-command-response triplets and the corresponding context. OpenFlamingo is a framework that allows multimodal models to generate image-based text and contextual examples.
During training, the Otter model follows the OpenFlamingo paradigm, freezing previously trained encoders and tuning specific modules. The training data follows a particular format with image, user instructions, responses generated by “GPT” and a [endofchunk] symbolic. The model is trained using cross-entropy loss, with the token separating solutions for prediction goals.
The authors integrated Otter into Hugging Face Transformers, allowing for easy reuse and integration into researchers’ pipelines. They optimized the model for training on 4×RTX-3090 GPUs and supported fully fragmented parallel data (FSDP) and DeepSpeed to improve efficiency. They also offer a script to convert the original OpenFlamingo checkpoint to the Hugging Face Model format. Regarding demos, Otter performs better in following user instructions and exhibits advanced reasoning skills compared to OpenFlamingo. Demonstrates the ability to handle complex scenarios and apply contextual knowledge. Otter also supports in-context multimodal learning and performs well on visual question-answering tasks, leveraging information from images and contextual examples to provide comprehensive and accurate answers.
In conclusion, this research contributes to multimodal models by introducing the MIMIC-IT dataset, improving the Otter model with better instruction following abilities and learning in context, and optimizing the OpenFlamingo implementation to facilitate accessibility. . Otter’s integration into Hugging Face Transformers allows researchers to take advantage of the model with minimal effort. Otter’s proven abilities to follow user instructions, reason through complex scenarios, and perform in-context multimodal learning showcase advances in multimodal understanding and generation. These contributions provide valuable resources and information for future research and development in multimodal models.
review the Paper, Project and Github. Don’t forget to join our 24k+ ML SubReddit, discord channel, and electronic newsletter, where we share the latest AI research news, exciting AI projects, and more. If you have any questions about the article above or if we missed anything, feel free to email us at [email protected]
featured tools Of AI Tools Club
🚀 Check out 100 AI tools at AI Tools Club
Mahmoud is a PhD researcher in machine learning. He also has a
bachelor’s degree in physical sciences and master’s degree in
telecommunication systems and networks. Your current areas of
the research concerns computer vision, stock market prediction and
learning. He produced several scientific articles on the relationship with the person.
identification and study of the robustness and stability of depths
networks
