Self-Training Knowledge Distillation for Low-Latency LLM Annotation
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Solution Overview
Problem
Large Language Models (LLMs) face high latency and computational costs in deployment, and traditional data labeling processes are time-consuming and expensive, necessitating a more efficient and cost-effective method for training smaller models in production environments.
Innovation Solution
Implementing a cascade of models for knowledge distillation, including LLM Cascade for Annotation (LCA) and LLM Self-Training for Annotation (LSTA), where a large-scale LLM annotates a subset of data, a medium-scale LLM fine-tunes a significant portion, and a small-scale LLM is trained using pseudo-labels generated by the medium-scale LLM, or the medium-scale LLM self-trains to generate labels for the small-scale LLM.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large-scale LLM is deployed for data annotation, then annotation quality is improved, but computational cost and latency increase
Solution Approach 1:
The patent segments the annotation task across multiple models of different scales. A large-scale LLM annotates only a subset of data (e.g., 500 samples) to establish high-quality training data, while smaller models handle the remaining annotation tasks. This segmentation allows the system to benefit from high-quality annotations without requiring the large-scale LLM to process all data, thus reducing overall computational cost while maintaining annotation quality.
Solution Approach 2:
The patent introduces smaller models as intermediaries between the large-scale LLM and the full dataset. The large-scale LLM generates initial annotations for a subset, which then serve as training data for smaller models. These smaller models act as intermediaries that can efficiently annotate the remaining data without requiring direct involvement of the computationally expensive large-scale LLM, thus reducing computational cost while maintaining quality through knowledge distillation.
2Measurement precision
If traditional human annotation processes are used, then data quality is improved, but time and cost increase
Solution Approach 1:
The patent implements self-service through automated model-based annotation systems. Instead of relying on human annotators, the system uses trained models (initially trained on a small subset annotated by a large-scale LLM) to automatically annotate the majority of data. This self-service approach dramatically increases annotation speed and reduces costs while maintaining acceptable data quality, as the models can process data much faster than human annotators.
Solution Approach 2:
The patent applies preliminary action by having a large-scale LLM annotate a small subset of data first to create high-quality training data. This preliminary annotation of a subset (e.g., 500 samples) is then used to train smaller models that can efficiently annotate the remaining data. This preliminary action ensures that the automated annotation process is initialized with high-quality examples, maintaining data quality while enabling rapid scaling of annotation productivity.
3Reliability
If a small-scale LLM is trained on all available data, then model performance is improved, but training time and computational resources increase
Solution Approach 1:
The patent extracts only the essential training signals needed for small-scale LLM training by having a large-scale LLM annotate a carefully selected subset of data (e.g., 500 diverse samples). This extracted subset contains the most informative examples for training, allowing the small-scale LLM to achieve good performance without requiring training on all available data. This extraction approach reduces training time and computational resources while maintaining model performance.
Solution Approach 2:
The patent changes the parameter of training data quantity from processing all available data to processing a optimized subset (e.g., 500 samples). By changing this parameter and using sophisticated sampling strategies to select the most informative subset, the system achieves efficient training that reduces time and computational resources while maintaining or even improving model performance through better data quality rather than quantity.
Data Source
AI summary
A plurality of data items associated with user-generated content is identified. A first subset of data items in the plurality of data items is annotated using a first machine learning (ML) model. The first ML model is trained based on the first plurality of labels generated for the first subset of data items. The first ML model is used to annotate a second subset of data items in the plurality of data items. A second ML model is trained based on a second plurality of labels generated based on the annotating of the second subset of data items.