Dynamic Augmented Prompt Vector Generation for Language Model Stability
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Solution Overview
Problem
Conventional language models are unstable and require extensive trial and error with different prompt words due to varying attention and sensitivity to different words, leading to less accurate outputs and inefficient use of resources.
Innovation Solution
A dynamic generation technique for augmented prompt vectors is introduced, which encodes textual prompt words and context data using an encoder machine learning model, processes the interim vector with a sequence generation model trained on sequences of vectors, and generates a robust augmented prompt vector for improved language model output.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If prompt learning is used with pre-trained language models, then few-sample learning ability is improved, but output accuracy deteriorates due to sensitivity to prompt word choices
Solution Approach 1:
The patent introduces an intermediary system consisting of an encoder model and a sequence generation model that transforms the original prompt into an augmented prompt vector. This intermediary processing layer mediates between the user's simple prompt and the language model's complex processing requirements, thereby improving output accuracy while maintaining few-sample learning ability.
Solution Approach 2:
The patent changes the parameter representation of the prompt by encoding the textual prompt into a vector representation and then generating an augmented prompt vector with enhanced semantic information. This parameter transformation from text to enhanced vector space makes the model's attention mechanisms more stable and less sensitive to minor prompt variations.
2Reliability
If different prompt words are tried to achieve accurate results, then output accuracy may be improved, but time consumption and computational resources increase
Solution Approach 1:
The patent performs preliminary action by pre-processing the prompt through encoding and augmentation before it reaches the language model. The encoder and sequence generation model prepare an optimized augmented prompt vector in advance, eliminating the need for iterative trial-and-error of different prompt words during actual usage.
Solution Approach 2:
The system performs self-service by automatically generating optimized prompt vectors without requiring manual intervention to try different prompt words. The encoder and sequence generation model autonomously process the original prompt and produce the augmented version, saving user time and effort.
3Productivity
If prompt learning is used, then learning efficiency is improved, but stability of model output deteriorates due to varying attention to different words
Solution Approach 1:
The patent creates a composite prompt representation by combining the original prompt encoding with additional semantic information generated by the sequence generation model. This composite augmented prompt vector integrates multiple information sources, making the model's attention more stable and consistent across different runs while preserving learning efficiency.
Data Source
AI summary
Techniques for dynamic prompt vector generation for improved language model predictions are provided. A textual prompt word and textual context data are received, and an interim vector is generated by encoding the textual prompt word and the textual context data using an encoder machine learning model. An augmented prompt vector is generated by processing the interim vector using a sequence generation machine learning model, the sequence generation machine learning model trained based on at least one sequence of vectors comprising a training prompt word, a training related word, and a plurality of intermediate vectors. Model output is generated by processing the augmented prompt vector using a language machine learning model.