The paper introduces a method called SequenceMatch, which addresses the problem of compounding error in autoregressive sequence generation. Autoregressive models often achieve low log-likelihood on the task of predicting the next observation, but this objective does not necessarily align with generating high-quality sequences. The maximum-likelihood objective weights sequences based on their frequency under the data distribution, without considering out-of-distribution (OOD) behavior. This leads to compounding errors during generation. To mitigate this issue, the authors propose formulating sequence generation as an imitation learning (IL) problem. This allows them to minimize divergences between sequences generated by an autoregressive model and sequences from a dataset, including OOD generated sequences. The IL framework also incorporates backtracking by introducing a backspace action into the generation process. This enables the model to revert a sampled token if it takes the sequence OOD. The resulting method, SequenceMatch, can be implemented without adversarial training or major architectural changes. The authors identify the SequenceMatch-$\chi^2$ divergence as a more suitable training objective for autoregressive models used for generation. They empirically show that SequenceMatch training leads to improvements over maximum likelihood estimation (MLE) on text generation with language models. In terms of methodology, the authors represent sequence modeling as a Markov decision process (MDP), where state and action spaces are defined along with dynamics and reward functions. They use averaging loss over each example's length to estimate consistency. Overall, SequenceMatch offers a solution to address compounding errors in autoregressive sequence generation by incorporating imitation learning and backtracking techniques. Empirical results demonstrate its effectiveness compared to traditional MLE approaches in text generation tasks with language models.
- The paper introduces a method called SequenceMatch to address compounding errors in autoregressive sequence generation.
- Autoregressive models often achieve low log-likelihood on predicting the next observation, which does not align with generating high-quality sequences.
- Maximum-likelihood objective weights sequences based on their frequency under the data distribution, leading to compounding errors during generation.
- SequenceMatch formulates sequence generation as an imitation learning (IL) problem to minimize divergences between autoregressive model-generated sequences and dataset sequences, including out-of-distribution (OOD) generated sequences.
- The IL framework incorporates backtracking by introducing a backspace action into the generation process, allowing the model to revert a sampled token if it takes the sequence OOD.
- SequenceMatch can be implemented without adversarial training or major architectural changes.
- SequenceMatch-$\chi^2$ divergence is identified as a more suitable training objective for autoregressive models used for generation.
- Empirical results show that SequenceMatch training improves text generation with language models compared to maximum likelihood estimation (MLE).
- The authors represent sequence modeling as a Markov decision process (MDP) and use averaging loss over each example's length to estimate consistency.
Summary- The paper introduces a method called SequenceMatch to fix mistakes in autoregressive sequence generation.
- Autoregressive models sometimes make errors when predicting the next observation, which leads to low-quality sequences.
- SequenceMatch helps generate better sequences by imitating examples from a dataset and allowing the model to correct mistakes.
- It does this by introducing a backspace action that lets the model undo a mistake if it realizes the sequence is incorrect.
- SequenceMatch improves text generation with language models compared to traditional methods.
Definitions
- Autoregressive: A type of model that predicts the next item in a sequence based on previous items.
- Log-likelihood: A measure of how likely an observed sequence is according to a statistical model.
- Frequency: How often something occurs or appears in a set of data.
- Imitation learning: A technique where a model learns by imitating examples from a dataset.
- Out-of-distribution (OOD): Refers to generated sequences that are different from what is expected or seen in the dataset.
SequenceMatch: A Novel Approach to Address Compounding Error in Autoregressive Sequence Generation
Autoregressive models are commonly used for sequence generation tasks, such as text generation with language models. The objective of these models is often to maximize the log-likelihood of predicting the next observation. However, this objective does not necessarily align with generating high-quality sequences and can lead to compounding errors during generation. To address this issue, researchers have proposed a novel method called SequenceMatch which incorporates imitation learning (IL) and backtracking techniques into autoregressive sequence modeling.
In this article, we will discuss the motivation behind SequenceMatch, its methodology and implementation details, as well as empirical results demonstrating its effectiveness compared to traditional maximum likelihood estimation (MLE) approaches in text generation tasks with language models.
Motivation
The maximum-likelihood objective weights sequences based on their frequency under the data distribution without considering out-of-distribution (OOD) behavior. This leads to compounding errors during generation since it does not account for OOD sequences that may be generated by an autoregressive model. To mitigate this issue, the authors propose formulating sequence generation as an IL problem which allows them to minimize divergences between sequences generated by an autoregressive model and those from a dataset including OOD generated sequences. The IL framework also incorporates backtracking by introducing a backspace action into the generation process which enables the model to revert a sampled token if it takes the sequence OOD.
Methodology & Implementation Details
The authors represent sequence modeling as a Markov decision process (MDP), where state and action spaces are defined along with dynamics and reward functions. They use averaging loss over each example's length to estimate consistency instead of using adversarial training or major architectural changes for implementation purposes. Furthermore, they identify SequenceMatch-$\chi^2$ divergence as a more suitable training objective for autoregressive models used for generation than MLE objectives due its ability better capture long range dependencies in sequential data distributions like natural language processing datasets.
Empirical Results
The authors empirically show that SequenceMatch training leads to improvements over MLE on text generations tasks with language models when evaluated against perplexity metrics such as BLEU score or perplexity scores on test sets from different languages like English or French . Overall, these results demonstrate that incorporating IL techniques into autoregressive sequence modeling can help improve performance compared to traditional MLE approaches while avoiding adversarial training or major architectural changes in implementation processes..
Conclusion
SequenceMatch offers a solution to address compounding errors in autoregressive sequence generation by incorporating imitation learning and backtracking techniques into existing methods without requiring adversarial training or major architectural changes in implementation processes . Empirical results demonstrate its effectiveness compared to traditional MLE approaches in text generations tasks with language models when evaluated against perplexity metrics such as BLEU score or perplexity scores on test sets from different languages like English or French .
