Uncertainty-Aware Sequence Modeling for Question Answering
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Solution Overview
Problem
Existing sequence-based machine learning models for question answering lack a reliable method to determine output confidence, leading to inaccuracies due to limited understanding of output confidence, out-of-domain data, ambiguity in inputs, inconsistent training data, and hallucinations.
Innovation Solution
The integration of risk estimates into a tree of thoughts (TOT) reasoning process allows for identifying logic breaks in model outputs, regenerating uncertain reasoning steps, and continuing the generation process to improve output quality and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sequence models are used for question answering, then natural language tasks can be performed, but output confidence and reliability are limited due to hallucinations and inconsistent training data
Solution Approach 1:
The patent introduces an intermediary uncertainty estimation module that sits between the sequence model and the output. This module estimates uncertainty values for model predictions without requiring fundamental changes to the underlying sequence model architecture, thereby improving reliability while maintaining manageable complexity
Solution Approach 2:
The patent replaces traditional confidence measurement mechanisms (such as softmax probabilities) with an uncertainty estimation approach that better reflects actual model confidence. This substitution provides more reliable uncertainty signals while avoiding the complexity of completely redesigning the model architecture
2Measurement precision
If risk estimates are integrated into tree of thoughts reasoning process, then output accuracy is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent applies risk estimates at each reasoning step in the tree of thoughts process before full generation is completed. This preliminary assessment allows early identification of problematic reasoning paths, enabling selective regeneration only where needed, thus improving accuracy without proportionally increasing computational complexity
Solution Approach 2:
The patent implements selective regeneration of reasoning steps based on risk thresholds rather than regenerating entire sequences or all steps. This partial action approach focuses computational resources only on uncertain portions of the reasoning process, improving output accuracy while controlling computational overhead
3Reliability
If risk estimates are calculated for each reasoning step, then logic breaks can be identified, but processing time and computational resources are consumed
Solution Approach 1:
The patent implements a feedback mechanism where risk estimates are calculated for each reasoning step and used to determine whether regeneration is needed. This feedback loop enables selective processing of only those steps that exceed risk thresholds, maintaining logic accuracy while minimizing unnecessary processing time
Data Source
AI summary
Described herein are systems and methods for improving accuracy of model output generation. A method can include obtaining a risk-aware model and a user input, applying the risk-aware model to the user input, receiving, based on the applying, model output and corresponding risk values, comparing the corresponding risk values to a threshold risk value, and regenerating the user input based on the comparing. The method can also include iteratively performing the applying, receiving, comparing, and regenerating using the regenerated user input until one or more processing conditions is met. The user input can be regenerated in response to determining that the corresponding risk values are greater than the threshold risk value. The model output can include one or more sequences in a train-of-thought (TOT) of the risk-aware model.