September 2023
Introducing Training Data Augmentation with LLM
We're thrilled to announce the integration of Language Model (LLM) capabilities into our training data augmentation process. This enhancement empowers our platform to dynamically augment training data, leading to improved model performance and accuracy.
Highlights:
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Dynamic Data Augmentation : Leveraging LLM, our platform dynamically augments training data by generating synthetic examples from existing dataset that enrich the diversity and quality of our dataset. This process enhances the robustness and generalization capabilities of our models, resulting in more accurate and reliable performance across various use cases.
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Adaptive Learning : With LLM-powered data augmentation, our models adapt and evolve over time by continuously learning from existing data patterns and user interactions. This adaptive learning approach ensures that our models stay up-to-date and effective in capturing evolving user intents and preferences.
Feedback Based on User Chat History
In this release, we're introducing a new feedback mechanism based on user chat history, enabling users to provide valuable feedback directly from their interaction history.
Highlights:
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User-Centric Feedback : This user-centric feedback loop enables us to gather actionable insights and continuously improve the conversational experience based on real-time user inputs.
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Data-Driven Insights : By analyzing user feedback aggregated from chat history, we gain valuable insights into user preferences, common pain points, and areas for improvement. These data-driven insights drive iterative enhancements to our models and algorithms, ensuring that our platform evolves in alignment with user needs.
