Model Cascade Knowledge Distillation for Low-Cost 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 method for data annotation that maintains quality.
Innovation Solution
Implementing a cascade of machine learning 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 annotates the remainder, and a small-scale LLM is fine-tuned using pseudo-labels generated by the medium-scale LLM, or the medium-scale LLM self-trains to generate pseudo-labels for the small-scale LLM.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large-scale LLMs are deployed for data annotation, then annotation quality is improved, but computational costs and latency increase
Solution Approach 1:
The patent segments the annotation task across multiple models of different scales. A large-scale LLM annotates a subset of data to ensure high quality, while a medium-scale LLM annotates the remainder. This segmentation allows the system to achieve high annotation quality on critical data while reducing overall computational costs by distributing the workload across models with different resource requirements.
Solution Approach 2:
The patent introduces a medium-scale LLM as an intermediary between the large-scale and small-scale models. The medium-scale model receives annotations from the large-scale model and uses them to generate pseudo-labels for the small-scale model, creating a knowledge distillation pipeline that transfers annotation quality from larger to smaller models while reducing computational burden.
2Measurement precision
If traditional human annotation processes are used, then data quality is maintained, but time and cost increase
Solution Approach 1:
The patent implements self-service annotation through automated LLM-based annotation systems. The large-scale and medium-scale LLMs automatically annotate data without human intervention, eliminating the time-consuming manual annotation process while maintaining high data quality through the knowledge distillation pipeline that ensures accurate label generation.
Solution Approach 2:
The patent changes the parameter of annotation speed by transitioning from human annotators to automated LLM-based annotation. The system processes data at machine speed while maintaining quality through multi-model validation and knowledge distillation, achieving both high speed and high accuracy that cannot be simultaneously achieved with traditional human annotation.
3Productivity
If small-scale models are used for production, then computational efficiency is improved, but annotation quality may deteriorate
Solution Approach 1:
The patent uses the medium-scale LLM as an intermediary to transfer knowledge from the large-scale model to the small-scale model. The medium-scale model generates high-quality pseudo-labels that serve as training data for the small-scale model, enabling the small model to achieve production-level efficiency while maintaining annotation quality through knowledge distillation from larger models.
Solution Approach 2:
The patent performs preliminary annotation using the large-scale LLM on a subset of data before deploying the small-scale model for production. This preliminary action creates a high-quality training dataset that enables the small-scale model to learn accurate annotation patterns, allowing it to maintain high annotation quality while operating efficiently in production environments.
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 ML model. A second ML model is trained based on the first plurality of labels generated for the first subset of data items. A second subset of data items in the plurality of data items is annotated using the second ML model trained. A third ML model is trained based on a second plurality of labels generated for the second subset of data items based on the annotating.