SequenceMatch: Imitation Learning for Autoregressive Sequence Modelling with Backtracking
(2306.05426)Abstract
In many domains, autoregressive models can attain high likelihood on the task of predicting the next observation. However, this maximum-likelihood (MLE) objective does not necessarily match a downstream use-case of autoregressively generating high-quality sequences. The MLE objective weights sequences proportionally to their frequency under the data distribution, with no guidance for the model's behaviour out of distribution (OOD): leading to compounding error during autoregressive generation. In order to address this compounding error problem, we formulate sequence generation as an imitation learning (IL) problem. This allows us to minimize a variety of divergences between the distribution of sequences generated by an autoregressive model and sequences from a dataset, including divergences with weight on OOD generated sequences. The IL framework also allows us to incorporate backtracking by introducing a backspace action into the generation process. This further mitigates the compounding error problem by allowing the model to revert a sampled token if it takes the sequence OOD. Our resulting method, SequenceMatch, can be implemented without adversarial training or architectural changes. We identify the SequenceMatch-$\chi2$ divergence as a more suitable training objective for autoregressive models which are used for generation. We show that empirically, SequenceMatch training leads to improvements over MLE on text generation with language models and arithmetic.
Overview
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SequenceMatch introduces a novel approach in autoregressive sequence modeling for text generation by leveraging imitation learning (IL) to address common issues such as compounding errors and out-of-distribution token generation.
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The method integrates a backtracking mechanism allowing for error correction and generates more accurate sequences without requiring adversarial training methods.
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SequenceMatch's performance surpasses traditional maximum likelihood estimation (MLE) objectives, showing improvements in text diversity and quality.
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The paper outlines the potential for future research in applying IL to sequence modeling, scalability of the method, and further developments in error correction mechanisms.
Imitation Learning Approach Enhances Autoregressive Sequence Modelling
Introduction to SequenceMatch
Recent advancements in autoregressive sequence modeling, especially within the domain of text generation, have shown promise across various applications, including machine translation, summarization, and creative writing assistance. A new method, SequenceMatch, introduces a novel approach that optimizes autoregressive models beyond traditional training objectives. Leveraging an imitation learning (IL) framework, SequenceMatch addresses the critical issues of compounding errors and out-of-distribution (OOD) token generation that often plague these models.
Key Innovations of SequenceMatch
SequenceMatch innovates in several crucial areas, presenting a comprehensive solution to longstanding challenges.
- Transition to Imitation Learning: At its core, SequenceMatch formulates the sequence generation task as an IL problem. This paradigm shift allows for the minimization of divergences between occupancy measures, which represent the distribution of sequences produced by the model and those in the dataset.
- Incorporating Backtracking: Uniquely, SequenceMatch integrates a backspace action into the generation process. This mechanism enables the model to backtrack from erroneously generated tokens, correcting its pathway to generate more coherent and contextually accurate sequences.
- No Need for Adversarial Training: The implementation of SequenceMatch sidesteps the complexities of adversarial training methods. It relies on a non-adversarial IL objective, simplifying the training process and improving the robustness of model outputs.
- SequenceMatch-χ Divergence: A significant contribution of this work is the identification of the SequenceMatch-χ divergence. This divergence criterion provides a more suitable objective for training autoregressive models focused on generation tasks.
Performance and Empirical Evaluation
The empirical evaluation of SequenceMatch demonstrates its effectiveness over the maximum likelihood estimation (MLE) objective, a standard benchmark in autoregressive model training. SequenceMatch shows notable improvements in general text generation, evidenced by superior performance on metrics such as MAUVE score and diversity, indicating enhanced quality and variety in generated text.
Theoretical Contributions and Practical Implications
This work contributes significantly to both the theoretical understanding and practical application of autoregressive sequence models. The IL-based approach offers a new perspective on minimizing divergence between model and data distributions, with backtracking introducing a practical mechanism for error correction during generation. These advancements suggest promising avenues for developing more capable and reliable generation models across various domains.
Future Directions in AI and Sequence Modelling
The introduction of SequenceMatch paves the way for future research exploring the potential of IL in sequence modeling and beyond. Future work may investigate the scalability of the method to larger models, its applicability to other types of generative tasks, and further innovations in divergence criteria that could offer even greater improvements in generation quality. Additionally, the impact of backtracking and other error-correction mechanisms on model interpretability and control warrants further exploration.
Conclusion
SequenceMatch represents a significant step forward in the development of autoregressive sequence models, offering a novel training methodology that addresses key challenges in the field. By grounding sequence generation in the IL framework and introducing backtracking as a corrective mechanism, SequenceMatch transcends traditional training limitations, presenting a robust, non-adversarial approach to model optimization. This work opens new paths for research and application, promising substantial advancements in text generation and other sequence modeling applications.
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