Normalized Sequential Model Projection for Sequence Consistency
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Solution Overview
Problem
Neural sequential models, particularly autoregressive models, face challenges in accounting for global properties of sequences, leading to issues like hallucinations and duplications in natural language generation and machine translation, especially when training data is limited.
Innovation Solution
A normalized sequential model is produced by projecting a sequential energy-based model onto a target autoregressive model, incorporating both local and global components to approximate a normalized distribution, allowing for better handling of global features and sequence properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If autoregressive models are used for sequence generation, then local token prediction is simplified and training is efficient, but global sequence properties are overlooked leading to hallucinations and duplications
Solution Approach 1:
The model is divided into two independent components: a local autoregressive component for token prediction and a global energy-based component for sequence-level constraints. This segmentation allows each component to specialize in its function, with the autoregressive part maintaining training efficiency and the energy-based part ensuring global consistency.
Solution Approach 2:
The patent merges the autoregressive model and energy-based model into a hybrid sequential model where the energy-based model provides global constraints and the autoregressive model handles local predictions. The combination is achieved through a projection operation that aligns the two models, allowing them to work together synergistically.
2Reliability
If energy-based models are used to capture global properties, then global sequence consistency is improved, but normalization becomes difficult and sampling becomes complex
Solution Approach 1:
The autoregressive model serves as an intermediary between the energy-based model and the final sampling process. The energy-based model provides unnormalized global constraints, the autoregressive model processes these constraints locally, and the projection operation normalizes the combined distribution, making sampling tractable.
Solution Approach 2:
The model transitions from working with unnormalized energy-based distributions to normalized probability distributions through the projection operation. This parameter change enables standard sampling techniques to be applied while retaining the global constraints imposed by the energy-based model.
3Reliability
If large training datasets are used, then autoregressive models can mitigate myopia, but data availability becomes a limiting factor
Solution Approach 1:
The energy-based model extracts and encodes global sequence properties into a separate component, isolating them from the autoregressive processing. This extraction allows the model to capture global constraints without requiring extensive training data, as the energy-based component can be trained to embody these global properties.
Solution Approach 2:
The model adds a global constraint dimension to the traditional autoregressive local prediction dimension. By operating in both local and global dimensions simultaneously, the model achieves better global property accounting without requiring proportional increases in training data quantity.
Data Source
AI summary
In a method for generating a normalized sequential model using a processor, a sequential energy-based model computed by a parameterized neural network is provided. The sequential energy-based model defines an unnormalized probability distribution over a target sequence for a context source. The normalized sequential model is generated by projecting the sequential energy-based model onto a target autoregressive model that approximates a normalized distribution associated with the sequential energy-based model.