Text Classification Using LLM-Derived Queries for Reliable Threat Detection
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Solution Overview
Problem
Existing text classification methods, particularly those using large language models (LLMs), struggle with unexplained errors and hallucinations, leading to misclassification of malicious content, and lack transparency in decision-making processes.
Innovation Solution
A system that combines LLMs with machine learning (ML) techniques to extract explanatory statements and queries from training datasets, transforming these into features for ML models to improve classification accuracy and provide transparent explanations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If large language models are used for text classification, then text processing capability is improved, but reliability deteriorates due to hallucinations and unexplained errors
Solution Approach 1:
The system segments the LLM's output into distinct components: explanatory statements, queries, and confidence scores. By separating these elements, the system can process and validate each component independently, reducing the impact of hallucinations while preserving the LLM's text processing capabilities.
Solution Approach 2:
The system introduces an intermediary processing layer that transforms LLM outputs into structured features. This intermediary layer acts as a buffer between the LLM and the final classification decision, allowing for validation and correction of LLM-generated content before it influences the classification outcome.
2Reliability
If traditional classification methods are used, then reliability is maintained, but explainability deteriorates due to lack of transparent decision-making processes
Solution Approach 1:
The system implements feedback mechanisms where explanatory statements and queries generated by the LLM are used to create human-readable explanations for classification decisions. This feedback loop ensures that traditional reliable classification methods are enhanced with transparent reasoning capabilities.
Solution Approach 2:
The system changes the parameter representation by transforming LLM-generated explanatory statements into structured features and queries. This parameter transformation enables the system to maintain stable classification while providing rich explanatory information about the decision-making process.
3Loss of information
If LLMs generate explanatory statements, then explainability is improved, but manufacturing precision deteriorates due to false information and hallucinations
Solution Approach 1:
The system dynamically processes LLM-generated explanatory statements by evaluating them against the trained classification model's actual decision logic. This dynamic evaluation allows the system to adapt explanations to match the true classification reasoning, correcting inaccuracies while maintaining explainability.
Solution Approach 2:
The system performs preliminary action by training a dedicated classification model on features derived from LLM outputs before deployment. This preliminary training phase allows the system to learn accurate patterns from LLM-generated explanations, enabling it to distinguish between accurate and hallucinated explanations in production.
4Reliability
If multiple processing steps are added to reduce hallucinations, then reliability is improved, but device complexity increases
Solution Approach 1:
The system merges multiple processing functions into an integrated pipeline: LLM output extraction, feature transformation, model training, and explanation generation are combined into a unified workflow. This merging reduces the apparent complexity by presenting a cohesive system rather than separate independent components.
Solution Approach 2:
The trained classification model serves multiple functions simultaneously: it performs accurate classification, generates explanations, and validates LLM outputs. This multi-functionality reduces the need for separate dedicated components, thereby reducing overall system complexity while maintaining high reliability.
Data Source
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AI summary
A computer-implemented method for text classification may include prompting, by a computing device, a large language model (LLM) to extract a set of explanatory statements for a training dataset, wherein each explanatory statement describes a difference between a grouping of samples within the training dataset. The method may also include prompting, by the computing device, the LLM to generate a set of queries based on the set of explanatory statements, wherein each query evaluates the difference described by an explanatory statement. The method may include training, by the computing device, a machine-learning (ML) model using the set of queries as features. Furthermore, the method may include classifying, by the computing device using the ML model, an unknown sample as a security threat. Finally, the method may include performing, by the computing device, a security action to mitigate the security threat. Various other methods, systems, and computer-readable media are also disclosed.