LLM Classification Confidence Feedback for Low-Error Responses
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Solution Overview
Problem
Generative language models (LLMs) often generate incorrect classifications due to probabilistic token selection, leading to downstream issues like wasted computational resources and user interaction problems, such as providing irrelevant or hallucinated responses.
Innovation Solution
Implementing a method to determine a confidence value based on the probability differences between the selected and alternative tokens in the LLM's output, allowing for actions like stopping or modifying the generation, providing additional prompts, or requesting clarification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If the LLM generates classification output based on probabilistic token selection, then the model can provide rapid responses, but the classification accuracy deteriorates due to probabilistic errors
Solution Approach 1:
The system implements feedback by computing confidence values from probability distributions and using them to control subsequent actions. The confidence value feedback loop allows the system to adjust its behavior based on the reliability of each classification, stopping generation when confidence is low or correcting errors when they occur.
Solution Approach 2:
The system changes the parameter of confidence threshold to control the trade-off between speed and accuracy. By adjusting the confidence threshold parameter, the system can rapidly classify high-confidence cases while spending more computational resources on low-confidence cases, thereby optimizing overall performance.
2Productivity
If the LLM continues generation after incorrect classification, then more output can be produced, but computational waste increases due to hallucinated or irrelevant content
Solution Approach 1:
The system performs preliminary classification with confidence assessment before committing to full generation. By evaluating the confidence value of the classification token first, the system can prevent wasteful generation of hallucinated content while still maintaining high productivity for confident classifications.
Solution Approach 2:
The system skips unnecessary generation steps when confidence is low by stopping generation early or requesting clarification. This allows the system to rapidly handle confident cases while avoiding the computational waste of generating incorrect content, thereby optimizing the productivity-energy trade-off.
3Reliability
If the system requests clarification for low-confidence classifications, then accuracy improves, but interaction time increases
Solution Approach 1:
The system uses the confidence value parameter to dynamically determine whether to request clarification. By setting an appropriate confidence threshold parameter, the system can quickly classify high-confidence cases without additional interaction while requesting clarification only for low-confidence cases, thereby minimizing overall interaction time while maintaining accuracy.
Data Source
AI summary
A large language model (LLM) may be used to classify an input into one of a plurality of categories. However, given the machine-learning operation of the LLM, the output of the LLM does not represent a definitive statement, but is based on probability computations of the machine learning model. Therefore, the classification performed by the LLM might not be correct. Classification into the wrong category by the LLM results in downstream technical problems. In some implementations, when an LLM generates a response that classifies an input, one or more probability values associated with a token that forms the basis of the response may be used to determine a confidence value. The confidence value is indicative of confidence in the classification performed by the LLM. An action may be taken based on the confidence value.