Popularity Bucketing for LLM Content Retrieval Bias
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Solution Overview
Problem
Pre-trained large language models are not well-suited for content item retrieval or recommendation of semantically relevant content items, as their training data may not align with specific tasks, business goals, and requirements, leading to biased and noisy training data.
Innovation Solution
Implementing a popularity bucketing strategy to debias training data by bucketizing content items based on popularity scores, using an ensemble of models to generate diverse and debiased training data, and aggregating top K matches from each content item bucket to form the training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If pre-trained large language models are used for content item retrieval, then the model has general language understanding capabilities, but the training data does not align with specific tasks and business goals, leading to biased and noisy results
Solution Approach 1:
The patent segments the training data into multiple buckets based on popularity scores (e.g., high popularity, medium popularity, low popularity). This segmentation allows the model to learn from diverse content items with different popularity levels, reducing bias toward only popular items while maintaining task alignment for content retrieval
Solution Approach 2:
The patent changes the parameter distribution of training data by controlling the proportion of content items from different popularity buckets. By adjusting these parameters (e.g., using equal proportions from each bucket), the training data better aligns with specific retrieval tasks and business goals, improving both adaptability and reliability
2Reliability
If training data includes only popular content items, then the model learns from high-quality data, but the training data becomes biased and lacks diversity
Solution Approach 1:
The patent divides the content item database into multiple segments or buckets based on popularity scores. Instead of using only popular items, the system segments data into high, medium, and low popularity groups, ensuring diverse representation in the training set while maintaining quality through controlled selection from each segment
Solution Approach 2:
The patent applies local quality by ensuring each popularity bucket contributes appropriately to the training data. Rather than uniform sampling, the system adjusts the quality and proportion of data from each local segment (bucket) to achieve overall balance, allowing high-quality data from popular items while incorporating diverse items from less popular buckets
3Adaptability or versatility
If the model is trained on generic training data, then the model has broad language capabilities, but the model does not generalize well to specific domains and tasks
Solution Approach 1:
The patent performs preliminary action by pre-processing and bucketizing content items based on popularity scores before training. This preliminary organization of data into balanced buckets ensures that when the model is trained, it immediately benefits from diverse, debiased training data that facilitates better domain adaptation and task-specific performance
Solution Approach 2:
The patent introduces an intermediary process (popularity-based bucketing and filtering) between the generic content database and the model training. This intermediary step transforms generic data into task-aligned training data by controlling the representation of different popularity levels, enabling better generalization to specific domains while maintaining measurable performance improvements
Data Source
AI summary
Pre-trained large language models may be trained on a large data set which may not necessarily align with specific tasks, business goals, and requirements. Pre-trained large language models can solve generic semantic relationship or question-answering type problems but may not be suited for content item retrieval or recommendation of content items that are semantically relevant to a query. It is possible to build a machine learning model while using transfer learning to learn from pre-trained large language models. Training data can significantly impact the performance of machine learning models, especially machine learning models developed using transfer learning. The training data can impact a model's performance, generalization, fairness, and adaptation to specific domains. To address some of these concerns, a popularity bucketing strategy can be implemented to debias training data. Optionally, an ensemble of models can be used to generate diverse training data.