LLM Pseudo-Label Generation for Data Annotation Efficiency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Data annotation for machine learning models is a time-consuming and challenging task, requiring significant human effort and expertise.
Innovation Solution
A method combining human expertise with the capabilities of a Large Language Model (LLM) to refine and enhance data annotation, using Generative Pre-trained Transformers (GPT) to generate pseudo-labels and improve annotation efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human data annotation is performed manually, then accuracy and quality of labeling can be maintained, but time consumption and labor effort increase significantly
Solution Approach 1:
The patent uses Large Language Models to generate pseudo-labels that copy and replicate the work of human annotators. The LLM creates synthetic training data by predicting labels for unlabeled documents, effectively copying the annotation task at scale without requiring proportional human effort for each data point.
Solution Approach 2:
The system enables self-service annotation where the LLM annotates itself using its own pre-trained knowledge and the prompts provided by users. The model processes unlabeled data independently, generating its own predictions without continuous human intervention, thus automating the annotation service.
2Productivity
If Large Language Models are used to generate pseudo-labels, then annotation efficiency and scalability improve, but the complexity of the system increases
Solution Approach 1:
The patent introduces performance metrics as an intermediary mechanism that bridges the LLM's output and the final training data. These metrics evaluate the quality of pseudo-labels and determine which data points should be used for training, mediating between the complex LLM generation process and the requirements for reliable training data.
Solution Approach 2:
The system incorporates feedback loops where performance metrics are used to refine and re-rank pseudo-labels generated by the LLM. Users can provide feedback on metric thresholds and criteria, which then feeds back into the annotation process to improve the quality and reliability of the training data over time.
3Measurement precision
If performance metrics are used to evaluate and refine LLM results, then the quality of training data improves, but the time required for evaluation and refinement increases
Solution Approach 1:
The patent performs preliminary actions by pre-defining performance metric thresholds and criteria before the annotation process begins. Users can set up evaluation criteria in advance, allowing the system to automatically assess and filter pseudo-labels without requiring time-consuming ad-hoc evaluation during the annotation process.
Solution Approach 2:
The system skips time-consuming manual evaluation by using automated performance metrics to rapidly assess the quality of LLM-generated labels. Instead of thorough manual review of each annotation, the system rushes through the evaluation process using computational metrics that quickly identify high-quality training data.
Data Source
AI summary
A method and Graphic User Interface (GUI) system of data analysis for training a discriminative machine learning model are provided. One or more sets of data are provided to a large language model (LLM) for a predefined task with or without prompts provided by a user; the results output by the LLM is compared with a set of initial human-supplied ground truth data generated by the user to produce performance metrics, the user may provide or update one or more prompts for the LLM based on the performance metrics till the performance metrics reach a threshold. The set of initial human-supplied ground truth data and the results of the LLM can be used to train a discriminative machine learning model.