Domain-Specific Language Model Pretraining via Dual-Objective Training
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Pre-trained language models with BERT-like architecture, trained using large unlabeled domain-agnostic corpora, exhibit sub-par performance when directly applied to domain-specific Natural Language Processing (NLP) tasks due to changes in vocabulary and language style, despite their superior performance in non-domain-specific tasks.
Innovation Solution
A pre-training method that utilizes domain-specific objectives, such as masked language modeling and auto-regression, along with domain-specific corpus structures and category labels to enhance the representation space, allowing for the development of a single natural language model that can perform multiple domain-specific tasks effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a language model is pre-trained using domain-agnostic objectives on large unlabeled corpora, then the model achieves superior performance in non-domain-specific NLP tasks, but the model exhibits sub-par performance when applied to domain-specific tasks due to vocabulary and language style changes
Solution Approach 1:
The patent applies preliminary action by pre-training the language model on domain-specific data before fine-tuning on domain-specific tasks. The model is first pre-trained on domain-specific corpora using domain-specific objectives to learn domain-relevant representations, then fine-tuned on task-specific data. This two-stage approach ensures the model has foundational domain knowledge before tackling specific tasks, resolving the contradiction between general performance and domain-specific performance.
Solution Approach 2:
The patent employs parameter changes by dynamically adjusting the training objectives based on the domain and task requirements. The system uses a combination of domain-agnostic objectives (like masked language modeling) and domain-specific objectives (such as next sentence prediction for legal documents). By changing the objective function parameters and weights based on domain characteristics, the model optimizes its performance for both general and domain-specific tasks simultaneously.
2Reliability
If domain-specific objectives are added to pretraining objectives, then the model achieves better performance for domain-specific tasks, but the training complexity and computational requirements increase
Solution Approach 1:
The patent applies merging by combining multiple training objectives into a unified pretraining framework. Instead of training separate models for different domains and tasks, the system merges domain-agnostic objectives (MLM, auto-regression) with domain-specific objectives (next sentence prediction, clause classification) into a single multi-objective training process. This consolidation reduces overall system complexity while maintaining high performance across diverse tasks.
Solution Approach 2:
The patent implements universality by designing a single language model architecture that can perform multiple functions through different training objectives. The model is trained to handle both domain-agnostic NLP tasks and domain-specific tasks using the same underlying architecture and parameter set. This multi-functional approach eliminates the need for separate specialized models, reducing computational resources and training complexity while improving domain-specific performance.
3Adaptability or versatility
If fine-tuning approaches are used to introduce task-specific parameters, then the model can be adapted to domain-specific tasks, but the model architecture and training process become more complex
Solution Approach 1:
The patent applies dynamics by making the training process adaptive and flexible rather than static. The system dynamically selects and weights different training objectives based on the specific domain and task at hand. During training, the model can switch between domain-agnostic and domain-specific objectives, and the fine-tuning process adapts the model parameters to match the specific task requirements. This dynamic approach maintains model simplicity while achieving high adaptability to domain-specific tasks.
Data Source
AI summary
A method, apparatus, non-transitory computer readable medium, and system of training a domain-specific language model are described. One or more aspects of the method, apparatus, non-transitory computer readable medium, and system include obtaining domain-specific training data including a plurality of domain-specific documents having a document structure corresponding to a domain, and obtaining domain-agnostic training data including a plurality of documents outside of the domain. The domain-specific training data and the domain-agnostic training data are used to train a language model to perform a domain-specific task based on the domain-specific training data and to perform a domain agnostic task based on the domain-agnostic training data.