Response Inference Using Latent Variable Phase Validation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing conversation models, particularly generation-based models, face limitations in generating diverse responses to the same user input, as they are often restricted by pre-trained encoders and decoders, and struggle to recognize various inputs effectively.
Innovation Solution
A processor-implemented response inference method that generates a latent variable vector in a latent variable region space, creating multiple responses by encoding the input, and uses a validation vector with a predetermined phase difference to validate and output responses, allowing for the generation of varied responses to the same user input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a pre-trained encoder and decoder are used to generate responses, then the model structure is simple and easy to implement, but the response diversity is limited and the model cannot effectively recognize various inputs
Solution Approach 1:
The latent variable region space is segmented into multiple regions, each corresponding to a different response type. The encoder generates a latent variable vector that is then mapped to specific regions to produce diverse responses. This segmentation allows the model to explore multiple response possibilities without fundamentally changing the encoder-decoder structure.
Solution Approach 2:
The patent introduces a latent variable region space as an additional dimensional layer between the encoder and decoder. By projecting the latent variable vector onto this region space and selecting regions based on phase differences, the model gains access to multiple response dimensions while keeping the base encoder-decoder architecture intact.
2Reliability
If multiple validation vectors with phase differences are generated to validate responses, then the response validation accuracy is improved, but the computational time and processing complexity increase
Solution Approach 1:
The validation vectors are generated in advance with predetermined phase differences (e.g., 90 degrees, 180 degrees) during the model training phase. This preliminary preparation allows the validation process to proceed efficiently during inference, as the phase difference relationships are pre-established rather than computed in real-time.
Solution Approach 2:
The patent changes the phase parameter of the latent variable vector to generate multiple validation vectors. By systematically varying the phase parameter (e.g., 0, 90, 180, 270 degrees), the model creates diverse validation samples that test different response possibilities without requiring complex computational transformations.
Data Source
AI summary
A processor-implemented response inference method and apparatus are disclosed. The response inference apparatus receives an input, generates a latent variable vector in a latent variable region space by encoding the input, generates a validation vector with a predetermined phase difference from the latent variable vector, generates an output response by decoding the latent variable vector, generates a validation response by decoding the validation vector, and validates the output response by comparing the output response to the validation response.