Domain-Specific Hallucination Detection for ML Resolutions
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Solution Overview
Problem
General-purpose machine learning models often produce inaccurate or resource-intensive results when applied to specific domains like IT incident handling, due to lack of sufficient training data or inability to generate domain-specific results, leading to hallucinated content that can be misleading or incorrect.
Innovation Solution
A domain-specific machine learning model trained on incident tickets with a resolution field and worklog field is used to generate accurate resolutions, which includes a hallucination detection system to identify and remove hallucinated content in real-time, using a hallucination score calculated from source data and training data, thereby ensuring trustworthiness of generated content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a general-purpose machine learning model is applied to a specific domain like IT incident handling, then the model can process various types of data, but the accuracy and reliability of generated resolutions deteriorates due to lack of domain-specific training data
Solution Approach 1:
The system segments the machine learning model into a general-purpose base model and a domain-specific fine-tuned model. The general-purpose model handles diverse data types while the domain-specific model specialized in IT incident handling provides accurate resolutions. This segmentation allows each model to optimize for its specific function without compromising the other.
Solution Approach 2:
The patent applies local quality by creating a domain-specific fine-tuned model that is optimized for IT incident handling while the general-purpose model maintains broad applicability. The fine-tuned model incorporates domain-specific knowledge and terminology, providing high accuracy for specific domains while the base model ensures versatility across different types of incident data.
2Adaptability or versatility
If a general-purpose machine learning model is trained on diverse data, then the model covers broad topics, but it consumes excessive computational resources to train and achieve sufficient accuracy for specific domains
Solution Approach 1:
The system divides the training process into two stages: pre-training the general-purpose model on diverse data once, and then fine-tuning the domain-specific model on domain-specific data. This segmentation allows the computationally intensive pre-training to be done once with diverse data, while subsequent fine-tuning uses minimal computational resources to adapt to specific domains.
Solution Approach 2:
The patent applies preliminary action by pre-training the general-purpose model on diverse data before domain-specific fine-tuning. This preliminary training establishes a robust foundation that can be efficiently adapted to specific domains with minimal additional computational resources, avoiding the need to retrain the entire model from scratch for each domain.
3Reliability
If a domain-specific machine learning model is trained on sufficient quality training data, then the model generates accurate resolutions, but the model cannot be trained if training data of sufficient quality is not available
Solution Approach 1:
The system incorporates feedback mechanisms where the hallucination detection system provides feedback on the quality of generated resolutions. This feedback is used to iteratively improve the domain-specific model, allowing the model to learn from its mistakes and improve accuracy over time even when training data is limited.
Solution Approach 2:
The patent applies self-service through the hallucination detection system that automatically identifies and corrects hallucinated content in generated resolutions. This self-correction mechanism allows the model to improve its own performance without requiring external intervention or additional high-quality training data, making the model more feasible to train and deploy.
4Reliability
If a hallucination detection system is implemented to identify and remove hallucinated content, then the accuracy and trustworthiness of generated resolutions improve, but the system complexity increases
Solution Approach 1:
The patent introduces a hallucination detection system as an intermediary component between the domain-specific model and the final output. This intermediary system analyzes generated resolutions, identifies hallucinated content, and provides corrections. The intermediary approach allows the complex hallucination detection functionality to be separated from the main model, making the overall system more manageable and easier to implement.
5Reliability
If real-time hallucination detection and correction is performed, then the accuracy of generated resolutions improves, but the processing time and computational resources increase
Solution Approach 1:
The system applies partial action by implementing hallucination detection and correction for the most critical and problematic areas of generated content rather than uniformly applying it to all content. The hallucination detection system prioritizes identifying and correcting the most likely hallucinated content based on domain-specific patterns and risk assessment, reducing processing time while maintaining high accuracy.
Data Source
AI summary
An incident ticket having a worklog field for a resolution field and a worklog providing a history of actions taken during attempts to resolve an incident may be received. The incident ticket may be processed using a domain-specific machine learning model trained using training data that includes a plurality of resolved incident tickets, to thereby generate at least one resolution statement. Source data used by the domain-specific machine learning model in providing the at least one resolution statement may be determined, the source data including one of the worklog and the training data. A hallucination score may be assigned to the at least one resolution statement, based on the source data, to identify hallucinated content within the at least one resolution statement. The at least one resolution statement may be modified to remove the hallucinated content and thereby obtain a resolution for inclusion in the resolution field.