Light Language Models for Natural Data Chart Insights
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Solution Overview
Problem
Existing data captioning systems face challenges in generating accurate and naturally phrased insights from data charts due to reliance on large language models that are computationally inefficient and prone to overfitting, leading to erroneous and mechanically phrased outputs.
Innovation Solution
A system that synthesizes and augments training data to anonymize and partition data charts, generates insight templates, and distills knowledge from a large language model into a light language model to improve accuracy and efficiency, using a data narrator model to guide natural model insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large language models are used for data captioning, then accuracy in generating naturally phrased captions is improved, but computational efficiency deteriorates due to high resource consumption
Solution Approach 1:
The patent creates a distilled language model that copies the essential captioning capabilities of a large language model into a smaller, more efficient model. The distilled model is trained on synthetic data generated by the large model, replicating its knowledge while using far fewer computational resources during inference.
Solution Approach 2:
The patent extracts the specific data captioning knowledge from the large language model and transfers it to a smaller model. This extraction process involves generating synthetic training data with the large model's expertise, then using that data to train a compact model that contains only the necessary captioning abilities.
2Reliability
If large language models are used for data captioning, then quality of insights is improved, but cost and complexity of the system worsens
Solution Approach 1:
The patent copies the insight generation quality of large language models into a distilled model that is simpler and less expensive to deploy. The synthetic training data preserves the high-quality reasoning patterns while the smaller model structure reduces system complexity.
Solution Approach 2:
The patent uses a cost-effective approach by training a small, disposable distilled model on synthetic data rather than deploying expensive large models. The distilled model can be easily deployed and replaced without significant infrastructure investment.
3Productivity
If synthetic training data is used to train light language models, then training efficiency is improved, but data quality may worsen due to potential inaccuracies in synthesized data
Solution Approach 1:
The patent uses the large language model as an intermediary to create high-quality synthetic training data. This intermediary process ensures that the synthetic data maintains accuracy by leveraging the large model's expertise, while still enabling efficient training of the distilled model.
Solution Approach 2:
The patent performs preliminary data synthesis and quality validation before training the distilled model. The synthetic training data is generated and validated in advance, ensuring high quality before the actual distillation process begins, which improves both efficiency and data quality.
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer readable media for generating naturally phrased insights about data charts using light language models distilled from large language models. To synthesize training data for the light language model, in some embodiments, the disclosed systems leverage insight templates for prompting a large language model for generating naturally phrased insights. In some embodiments, the disclosed systems anonymize and augment the synthesized training data to improve the accuracy and robustness of model predictions. For example, the disclosed systems anonymize training data by injecting noise into data charts before prompting the large language model for generating naturally phrased insights from insight templates. In some embodiments, the disclosed systems further augment the (anonymized) training data by splitting or partitioning data charts into folds that act as individual data charts.