Domain Specialty Instruction Generation for Text Analysis
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Solution Overview
Problem
Large language models (LLMs) often introduce incorrect or nonsensical statements, known as 'hallucinations,' in their output, which can lead to decreased user trust and potentially negative impacts in critical applications like healthcare.
Innovation Solution
Implementing domain specialty instruction generation for text analysis tasks, where machine learning models are trained to accept and apply domain-specific information as input, reducing the need for multiple domain-specific models and allowing for faster development and deployment of systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If domain-specific models are trained for each specialty, then accuracy in domain-specific text analysis is improved, but development time and cost increase
Solution Approach 1:
The patent applies universality by creating a single base model that can perform multiple text analysis tasks across different domains through prompt engineering and domain-specific instruction tuning, eliminating the need to train separate models for each domain while maintaining high accuracy
2Measurement precision
If domain-specific models are trained for each specialty, then accuracy in domain-specific text analysis is improved, but resource consumption increases
Solution Approach 1:
The patent reduces resource consumption by using a single universal model that can be adapted to different domains through prompt engineering and instruction tuning, rather than training and maintaining multiple separate domain-specific models
Solution Approach 2:
The patent uses prompt templates and instruction sets that can be copied and adapted across different domains, allowing the same base model to be applied universally without requiring domain-specific model training
3Productivity
If LLMs generate text without domain-specific training, then development speed is improved, but factual accuracy deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-tuning the model with domain-specific instructions and knowledge before deployment, so that when the model generates text, it already has the necessary domain knowledge embedded without requiring full domain-specific training
Solution Approach 2:
The patent changes the instructional parameters and prompt structures to incorporate domain-specific knowledge, allowing the model to generate factually accurate text by modifying how it processes and generates information rather than retraining the entire model
Data Source
AI summary
Domain specialty instructions may be generated for performing text analysis tasks. An input text may be received for performing a text analysis task. A domain specialty may be identified for the input text. Specialty domain identifiers may be inserted as part of generating instructions to perform the text analysis task using a pre-trained large language model fine-tuned to a domain that includes multiple domain specialties. The pre-trained large language model may perform the text analysis task on the input text using the generated instructions. A result of the text analysis tsk performed on the input text may be provided.