Visual-Imagination Language Ensemble for Zero-Shot Prediction Bias
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Solution Overview
Problem
Large-scale pretrained language models trained solely on text suffer from human reporting bias, as the frequency of textual statements does not always correspond to their relative likelihood in the real world, necessitating supplementation with other modalities.
Innovation Solution
A method and apparatus that integrate an image conversion model and a language model to create a model ensemble, enabling the processing of task and solution input streams to generate accurate predictions in a zero-shot environment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If language models are trained solely on text data, then training simplicity is maintained, but accuracy deteriorates due to human reporting bias
Solution Approach 1:
The patent combines text-only language models with vision-language models into an ensemble system. The text-only model processes textual queries while the vision-language model generates visual representations, and their predictions are merged through averaging to produce final outputs, thereby eliminating human reporting bias present in text-only training data
Solution Approach 2:
Visual representations serve as an intermediary between the query and answer pairs. The vision-language model converts textual queries into visual forms and answers into visual forms, allowing the system to learn from visual data that is free from human reporting bias while maintaining the ability to process natural language inputs and outputs
2Measurement precision
If model ensemble is created with image conversion model and language model, then accuracy is improved, but device complexity increases
Solution Approach 1:
The vision-language model serves multiple functions: it acts as an image conversion model that transforms textual queries into visual representations, and simultaneously serves as a language model that processes and generates natural language. This multi-functionality reduces the need for separate specialized components, thereby managing system complexity while maintaining accuracy improvements
Solution Approach 2:
The ensemble system is segmented into distinct functional components: the text-only language model handles textual processing, the vision-language model handles visual representation and generation, and the ensemble mechanism handles prediction aggregation. This segmentation allows each component to be optimized independently while working together to achieve improved accuracy
Data Source
AI summary
A language processing method performed by at least one processor includes receiving a task input stream and a solution input stream; selecting one of the task input stream and the solution input stream, and providing the selected stream to an image conversion model; creating, based on the selected input stream, a model ensemble of the image conversion model and the language model; and outputting a prediction based on the model ensemble.