Energy-Based Model for Attribute Distribution Control in Language Models
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Solution Overview
Problem
Existing fine-tuning methods for pre-trained language models risk catastrophic forgetting and lack distributional control, leading to lesser quality models with imbalanced attribute generation.
Innovation Solution
A method that uses an energy-based model to minimize divergence from a prior distribution while satisfying target constraints, allowing for both pointwise and distributional control over attribute generation in text models, ensuring the target model inherits favorable linguistic qualities from the pre-trained model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If existing fine-tuning methods are used to control text generation attributes, then the model can generate text with desired attributes, but the model quality deteriorates due to catastrophic forgetting
Solution Approach 1:
The method performs preliminary actions by computing importance weights and constructing an energy-based model before fine-tuning, using the pre-trained model's knowledge to guide the controlled generation process and prevent catastrophic forgetting
Solution Approach 2:
The method changes parameters by introducing importance weights and energy-based model parameters that control the distribution of attributes during text generation, allowing flexible control without retraining the entire model
2Adaptability or versatility
If existing fine-tuning methods are used to control text generation attributes, then the model can generate text with desired attributes, but the distributional control is insufficient leading to imbalanced attribute generation
Solution Approach 1:
The method implements feedback by using importance weights that are computed based on the desired attribute distributions and applying them during text generation to steer the output towards target distributions
Solution Approach 2:
The method introduces dynamic control through energy-based models and importance weights that can be adjusted to control the distribution of attributes, enabling both pointwise and distributional control over generated text
3Manufacturing precision
If pointwise control is applied over attributes focusing on individual output quality, then each generated text can meet attribute requirements, but collective statistical properties and distributional balance are lost
Solution Approach 1:
The energy-based model serves multiple functions simultaneously: it maintains pointwise control over individual text quality while also enforcing distributional constraints across the entire generated corpus, achieving both precision and balance
Data Source
AI summary
A method for generating a language model for text generation by receiving a pre-trained language model having attributes with existing probability distributions over the pre-trained language model; receiving at least one target constraint; the target constraint specifying an expectation of a target attribute over a language model that approximates the pre-trained language model; computing parameters of an energy based model by applying the target constraint to the pre-trained language model; obtaining samples from a reference policy; updating parameters of a target policy using the obtained samples and the energy based model; updating the reference policy with the target policy if the target policy is superior to the reference policy; and outputting the target policy as a target language model. The target language model is adapted to generate text with the target attribute over a probability distribution that approximates the desired probability distribution specified by the target constraint.