Improving Autoregressive Models for Coherent Sequence Generation: Introducing SequenceMatch

Autoregressive models, such as GPT-3, are statistical models that predict the future value of a variable based on its past values. These models learn patterns and relationships in language by training on a large text corpus. However, smaller models or models with lower generation temperatures often generate repetitive or erroneous outputs. To address this, Stanford researchers developed a method called SequenceMatch that improves the coherence and quality of generated text.

SequenceMatch solves two main challenges faced by autoregressive models. First, it minimizes the χ2-divergence between actual data and generated sequences, which improves performance compared to maximum likelihood estimation (MLE). Second, it introduces a action that allows the model to erase the previous token and correct any errors. The researchers reformulated the problem of sequence generation as a reinforcement learning problem, reducing the divergence between the model and the data distribution.

Experimental evaluations showed that models fine-tuned on SequenceMatch performed better than MLE-trained models. They generated text that closely resembled the dataset and appeared more fluent and error-free. However, SequenceMatch requires more computational resources and time for generating lengthy texts. Future work will focus on studying different divergence methods and their impact on sequence quality.

For more information, you can check out the paper [here]( Don’t forget to join our ML SubReddit, Discord Channel, and Email Newsletter for the latest AI research news and projects. If you have any questions or feedback, feel free to email

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