Unified Auto-regressive Language Model for Sequence Evaluation
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Solution Overview
Problem
Existing neural network systems require separate generator and discriminator models, leading to increased computational overhead and memory usage when evaluating output sequences, which is inefficient for devices with memory constraints or high latency requirements.
Innovation Solution
Implementing an auto-regressive language model neural network that functions as both a generator and a discriminator, allowing for on-device evaluation of generated sequences with minimal additional computational overhead and memory usage, thereby improving output quality without requiring additional data communication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate generator and discriminator models are used, then evaluation accuracy is improved, but computational overhead and memory usage increase
Solution Approach 1:
The patent combines the generator and discriminator models into a unified neural network architecture where the same model performs both sequence generation and evaluation functions. This merging eliminates the need for separate discriminator infrastructure, reducing computational overhead and memory usage while maintaining evaluation capability through the model's inherent understanding of sequence quality
Solution Approach 2:
The neural network model is designed to perform multiple functions: it acts as a generator to create output sequences and as a discriminator to evaluate those sequences. This multi-functionality is achieved through the model's ability to process sequences in different modes, allowing a single model to replace what traditionally required two separate models
2Measurement precision
If separate generator and discriminator models are used, then evaluation capability is improved, but memory usage increases
Solution Approach 1:
The patent merges the generator and discriminator into a single neural network model, eliminating the need to load and maintain separate model parameters in memory. This consolidation significantly reduces memory usage on devices with constrained resources while preserving the model's ability to evaluate sequences through its trained understanding of quality metrics
3Measurement precision
If separate generator and discriminator models are used, then evaluation accuracy is improved, but latency increases
Solution Approach 1:
By combining generator and discriminator functions into a single model, the patent eliminates the time required to transfer sequences between models and performs evaluation in the same computational context. This single-model approach reduces latency by avoiding inter-model communication overhead and enabling more efficient processing pipelines
4Device complexity
If a single model is used for both generation and evaluation, then computational overhead is reduced, but evaluation accuracy may decrease
Solution Approach 1:
The neural network is designed with universal capabilities to perform both generation and evaluation through different processing modes. The model's training process equips it with the knowledge to assess sequence quality, allowing it to function as both creator and critic of output sequences without requiring specialized discriminator architecture
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for evaluating candidate output sequences using language model neural networks. In particular, an auto-regressive language model neural network is used to generate a candidate output sequence. The same auto-regressive language model neural network is used to evaluate the candidate output sequence to determine rating scores for each of one or more criteria. The rating score(s) are then used to determine whether to provide the candidate output sequence.