Multimodal Agent Image-Text Automation with Selective Human Feedback
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Solution Overview
Problem
Existing deep learning models require large amounts of labeled data for training, which is time-consuming and costly, and there is a need for systems that can integrate human knowledge and experience to enhance model performance and adapt to diverse tasks.
Innovation Solution
A system for automating multimodal agentic workflows using a multimodal agent that processes text and images, incorporating human-in-the-loop methods and active learning to construct prompts and automate interface workflows, utilizing a decoder-only Transformer logic for efficient training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If large amounts of labeled data are used for training deep learning models, then model performance is improved, but training time and cost increase significantly
Solution Approach 1:
The system performs preliminary actions by using human experts to annotate only a small subset of critical training data before model training. This preliminary human annotation provides high-quality seed data that guides the model learning process, eliminating the need to manually annotate large volumes of data and significantly reducing training time while maintaining model performance.
Solution Approach 2:
The system introduces an intermediary mechanism where human experts selectively annotate only the most informative or critical data samples, which then serve as a mediator to guide the model's learning process. This intermediary human-in-the-loop annotation approach allows the model to achieve high performance with minimal labeled data, reducing both training time and annotation costs.
2Reliability
If large amounts of labeled data are used for training deep learning models, then model performance is improved, but annotation cost increases significantly
Solution Approach 1:
The system performs preliminary selective annotation by human experts on a small subset of critical data samples before model training. This preliminary action provides high-quality seed data that guides subsequent automated learning, eliminating the need for expensive manual annotation of large volumes of data while maintaining model performance.
Solution Approach 2:
The system introduces human expert annotation as an intermediary mechanism that selectively labels only the most informative data samples. This intermediary approach acts as a efficient bridge between unannotated data and model training, achieving high performance with minimal annotation cost by focusing human expertise on critical cases only.
3Adaptability or versatility
If human knowledge is integrated into the modeling process, then model adaptability to diverse tasks is improved, but system complexity increases
Solution Approach 1:
The system introduces human experts as intermediaries who provide domain knowledge and contextual understanding during the modeling process. These human intermediaries guide the model's learning on diverse tasks by annotating critical data and providing feedback, enhancing model adaptability while managing system complexity through selective human involvement rather than complete manual processing.
Solution Approach 2:
The system implements a dynamic human-in-the-loop framework where human knowledge integration is adaptive and flexible. Humans intervene selectively based on task complexity and data criticality, allowing the system to dynamically adjust the level of human involvement. This dynamic approach enhances adaptability to diverse tasks while avoiding the overhead of constant complex human-machine interaction.
Data Source
AI summary
A system for image-text agentic interface automation is disclosed. A multimodal agent is configured to process arbitrary-length text sequences and arbitrary-resolution images. A newline insertion logic is configured to interleave a newline character between successive lines of image patches in a plurality of lines of image patches, wherein the newline character specifies an end of a line in an input image. A tokenization logic is configured to translate the input text sequence into a sequence of input text tokens, and to translate the successive lines of image patches interleaved with the newline character into a sequence of input image tokens. A linear projection logic is configured to linearly project a single token stream of the sequence of input text tokens and the sequence of input image tokens into a decoder-only Transformer logic, wherein the linear projection of the single token stream bypasses any embedding lookup.