Embedding Model Weight Customization for Domain Retrieval
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Solution Overview
Problem
Pre-trained language models are ineffective for domain-specific tasks due to their generalization from broad datasets, leading to poor performance in content retrieval when queries are ambiguous or partial, as they rely on exact match similarity measures that underperform in such scenarios.
Innovation Solution
A content retrieval system customizes a pre-trained embedding model by transforming it using a target domain dataset, modifying its weights based on target embeddings, and generating an efficacy score to improve performance in domain-specific tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If pre-trained embedding models are used for content retrieval, then the system can handle broad topics and general queries, but the model performance deteriorates on domain-specific tasks and ambiguous queries
Solution Approach 1:
The patent applies parameter changes by modifying the embedding model's weights through fine-tuning on domain-specific data. The model starts with pre-trained weights and updates them by computing loss based on domain embeddings, then applying gradient descent to adjust weights. This transforms the model from a general-purpose model to a domain-optimized model, resolving the contradiction between generalization capability and domain-specific accuracy.
Solution Approach 2:
The patent implements preliminary action by performing pre-training of the embedding model on general data before fine-tuning on domain-specific data. This two-stage approach allows the model to first learn general language representations and then adapt to specific domain requirements, maintaining both generalization capability and domain-specific performance.
2Productivity
If exact match similarity measures are used for content retrieval, then the system can process queries efficiently, but the retrieval accuracy deteriorates for ambiguous and partial queries
Solution Approach 1:
The patent replaces the mechanical exact match similarity measure with a neural network-based embedding model that uses cosine similarity on transformed embeddings. This substitution allows the system to handle ambiguous and partial queries effectively by capturing semantic relationships rather than relying on exact string matching, thereby improving retrieval accuracy while maintaining processing efficiency.
3Ease of manufacture
If pre-trained models are applied to target domains without customization, then the system implementation is simple, but the model fails to capture domain-specific nuances and terminology
Solution Approach 1:
The patent applies parameter changes through fine-tuning, where the model's weights are updated by computing gradients based on domain-specific embeddings and loss functions. This process maintains the simplicity of using pre-trained models while significantly improving domain terminology accuracy by adapting the model parameters to the target domain's specific vocabulary and patterns.
Data Source
AI summary
Disclosed herein are system, apparatus, article of manufacture, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for model customization for domain-specific tasks. An embodiment may select a pre-trained embedding model trained with a first dataset. The embodiment may determine a second dataset for a target domain. Based on target embeddings for data indicative of the target domain. The embodiment may transform the second dataset from a first format to a second format associated with the target domain. The embodiment may modify the weights of the pre-trained embedding model based on the transformed second dataset. Based on the modified weights, the embodiment may transform the pre-trained embedding model into a target embedding model for the target domain. The embodiment may then generate an efficacy score for the target embedding model based on a task of the target domain performed by the target embedding model.