LLM Prompt Decoy Categories for Reliable Classification
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Solution Overview
Problem
Large language models (LLMs) often hallucinate and produce false outputs that do not match user intent, particularly in categorization tasks due to linguistic or contextual similarities leading to incorrect category selection and API calls.
Innovation Solution
Incorporating decoy categories in LLM prompts that are closely aligned linguistically but unsupported, causing the LLM to gravitate towards these categories when incorrect, signaling misinterpretation and ensuring accurate responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If LLM is used for categorization tasks, then natural language processing capability is improved, but hallucination and incorrect category selection occur
Solution Approach 1:
The patent introduces an intermediary verification mechanism between the LLM's category selection and the final output. A software application acts as a mediator to validate whether the selected category matches the actual user intent by analyzing the full context, thereby preventing hallucinations while preserving the LLM's natural language processing capabilities
Solution Approach 2:
The system implements a feedback loop where the software application monitors LLM outputs for potential hallucinations, validates category selections against ground truth or contextual evidence, and provides corrective feedback to improve future categorization accuracy, thus enhancing reliability without sacrificing versatility
2Adaptability or versatility
If more categories are provided to LLM, then categorization coverage is improved, but likelihood of spurious selection increases
Solution Approach 1:
The software application serves as an intermediary that receives the LLM's category selection from the expanded category list and validates it against the actual user intent. This mediator filters out spurious selections even when many categories are available, maintaining both broad coverage and high precision
Solution Approach 2:
The system performs preliminary validation and verification steps before finalizing category selection. The software application pre-checks potential category assignments against contextual evidence and user intent indicators, preventing incorrect selections before they are committed, thus maintaining precision across diverse category options
Data Source
AI summary
In one embodiment, a device includes a processor configured to execute a software application to populate a large language model (LLM) prompt template yielding a populated LLM prompt including a categorical question for an LLM to perform a categorization task, the categorical question including given categories and decoy categories, provide the populated LLM prompt as input to the LLM, and receive a text response from the LLM based on processing the populated LLM prompt as input, the text response of the LLM including a categorical answer indicating one of the given categories or one of the decoy categories, and a memory to store data used by the processor.