LLM Uncertainty Decomposition for In-Context Learning
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Solution Overview
Problem
Large Language Models (LLMs) face challenges in determining the source of uncertainty in their outputs, which can be attributed to biased data, overfitting, underfitting, inaccurate labeling, inadequate training data, and other intrinsic limitations.
Innovation Solution
A method is provided to decompose LLM uncertainty into Aleatoric Uncertainty (AU) and Epistemic Uncertainty (EU) by prompting the LLM with text data outside pre-inference trained categories and test prompts with known ground truth, calculating total uncertainty, and selecting different LLM model parameters to isolate and rate the uncertainties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If LLMs use in-context learning to improve adaptability to new tasks, then versatility is improved, but uncertainty in outputs increases due to hallucination and lack of confidence estimation
Solution Approach 1:
The patent segments uncertainty into two distinct components: aleatoric uncertainty (inherent data variability) and epistemic uncertainty (model knowledge gaps). This segmentation is achieved through separate calculation methods - aleatoric uncertainty is computed from output probability distributions, while epistemic uncertainty is estimated through ensemble model predictions. By dividing the unified uncertainty concept into separable components, the system can address each type differently, improving overall reliability assessment.
Solution Approach 2:
The patent introduces an intermediary uncertainty estimation module that sits between the LLM's in-context learning process and the final output. This module calculates confidence scores by processing the model's internal probability distributions and comparing ensemble predictions, providing an intermediate confidence assessment that bridges the gap between flexible in-context learning and reliable output validation.
2Adaptability or versatility
If LLMs process text data outside pre-trained categories to improve adaptability, then versatility is improved, but measurement precision of output reliability deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-calculating uncertainty metrics during the inference process itself. Before finalizing outputs for unseen categories, the system computes aleatoric uncertainty from the probability distribution and epistemic uncertainty through ensemble comparisons. This preliminary uncertainty assessment allows the system to adapt to new categories while maintaining precision in reliability measurement through pre-computed confidence scores.
Solution Approach 2:
The patent changes parameters by utilizing multiple LLM configurations with different temperature settings and prompt variations. By adjusting these parameters and comparing results across multiple runs, the system maintains measurement precision for uncertainty estimation even when processing text data outside pre-trained categories. The parameter variations enable robust confidence scoring for novel inputs.
3Measurement precision
If multiple LLM parameters are tested to decompose uncertainty, then measurement precision of uncertainty sources is improved, but device complexity increases
Solution Approach 1:
The patent extracts the uncertainty analysis from the main LLM inference process by using a separate ensemble of models specifically for uncertainty estimation. Instead of complicating the primary model with multiple parameter tests, the system extracts uncertainty information through parallel ensemble predictions and probability distribution analysis. This extraction approach maintains measurement precision while avoiding direct complexity multiplication in the core inference path.
Solution Approach 2:
The patent uses copying by creating simplified ensemble models that replicate the main LLM's architecture but are optimized for uncertainty estimation. These copied models run in parallel with reduced computational overhead, allowing comprehensive parameter testing for uncertainty decomposition without proportionally increasing overall system complexity. The copied models focus specifically on generating confidence scores rather than full inference.
Data Source
AI summary
Methods and systems for prompting a Large Language Model (LLM) with a set of text data outside pre-inference trained categories and a test prompt for an initial parameter which has a known ground truth, calculating an uncertainty of an LLM's output, selecting another LLM model parameter and calculating the total uncertainty of the LLM's output with the other LLM model parameter. The methods and systems further include prompting the LLM with another test prompt, with the initial LLM parameter and the other LLM parameter, and calculating the total uncertainty of the LLM's output for initial LLM model parameter and the other LLM model parameter, decomposing the total uncertainty of the LLM into Aleatoric Uncertainty (AU) and Epistemic Uncertainty (EU) components, and rating the total uncertainty of the LLM, using the decomposed total uncertainty as a metric.