Multimodal AI Agent Training Data for Low-Annotation Software Automation
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Solution Overview
Problem
Existing deep learning models require large amounts of labeled data for training, which is laborious and time-consuming, and there is a high degree of coupling between tasks and data, limiting their performance and scalability.
Innovation Solution
Integrate human-in-the-loop (HITL) methods to incorporate human knowledge and experience, using core set construction and active learning to select key samples for training, and develop a system for automating software usage with multimodal agentic workflows.
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 labor and time requirements increase significantly
Solution Approach 1:
The system performs preliminary actions by using unlabeled data to generate synthetic labels and predictions before final model training. The prediction engine creates initial predictions that serve as pre-processed training data, reducing the need for time-consuming manual annotation of all training samples.
Solution Approach 2:
The prediction engine acts as an intermediary between unlabeled data and the training process. It generates synthetic labeled data by making predictions on unlabeled samples, which then serve as intermediate training data to improve model performance without requiring direct manual annotation of all data.
2Measurement precision
If more training data is collected to improve model accuracy, then model precision increases, but the complexity of data management and processing increases
Solution Approach 1:
The system performs self-service by automatically generating synthetic training data through the prediction engine. The engine uses the model's own predictions on unlabeled data to create training samples, eliminating the need for complex external data collection and management processes.
Solution Approach 2:
The data processing is segmented into distinct components: the prediction engine handles synthetic label generation, the training data generator manages data creation, and the model training process uses this processed data. This segmentation reduces overall system complexity by dividing data management tasks into specialized modules.
3Reliability
If human-in-the-loop methods are used to incorporate human knowledge, then model performance with minimal data is improved, but system complexity increases
Solution Approach 1:
The system applies partial action by selectively involving human expertise only when necessary. The prediction engine automatically handles most predictions, and human-in-the-loop mechanisms are activated only for uncertain or critical cases, reducing overall system complexity while maintaining high performance on minimal data.
Data Source
AI summary
A system for automating software usage includes an agent configured to automate. The agent is trained on one or more training data sets. The one or more training datasets include one or more of a first training dataset including documents containing text interleaved with images, a second training dataset including text embedded in images, a third training dataset including recorded videos of software usage, a fourth training dataset including portable document format (PDF) documents, a fifth training dataset including recorded videos of software tool usage trajectories, a sixth training dataset including images of open-domain web pages, a seventh training dataset including images of specific-domain web pages, and/or an eighth training dataset including images of agentic trajectories of the agent performing interface automation task workflows.