The Curious Case of Degeneration: Difference between revisions
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Donya Hamzeian | Donya Hamzeian | ||
== Introduction == | == Introduction == | ||
Text generation is the act of automatically generating natural language texts like summarization, neural machine translation, fake news generation and etc. Degeneration happens when the output text is incoherent or produces repetitive results. For example in figure | Text generation is the act of automatically generating natural language texts like summarization, neural machine translation, fake news generation and etc. Degeneration happens when the output text is incoherent or produces repetitive results. For example in the figure below, the GPT2 model tries to generate the continuation text given the context. On the left side, the beam-search was used as the decoding strategy which has obviously stuck in a repetitive loop. On the right side, however, you can see how the pure sampling decoding strategy has generated incoherent results. | ||
[[File: GPT2_example.png |caption=Example text|center |800px|caption position=bottom]] | [[File: GPT2_example.png |caption=Example text|center |800px|caption position=bottom]] | ||
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Some may raise this question that the problem with beam-search may be due to search error i.e. they are more probable phrases that beam search is unable to find, but the point is that natural language has lower per-token probability on average and people usually optimize against saying the obvious. | Some may raise this question that the problem with beam-search may be due to search error i.e. they are more probable phrases that beam search is unable to find, but the point is that natural language has lower per-token probability on average and people usually optimize against saying the obvious. | ||
The authors blame the long, unreliable tail in the probability distribution of tokens that the model samples from i.e. vocabularies with low probability frequently appear in the output text. So, top-k sampling with high values of k may produce texts closer to human texts, yet they have high variance in likelihood leading to incoherency issues. | |||
Therefore, instead of fixed k, it is good to dynamically increase or decrease the number of candidate tokens. Nucleus Sampling which is the contribution of this paper does this expansion and contraction of the candidate pool. | |||
==Language Model Decoding== | ==Language Model Decoding== | ||
There are two types of generation tasks. | |||
1. Directed tasks: In these tasks, there are pairs of (input, output) that the model tries to generate the output text which is tightly scoped by the input text. Because of this constraint, these tasks less suffer from the degeneration. Summarization, neural machine translation, and input-to-text generation are some examples of these tasks. | |||
2. Open-ended tasks like conditional story generation or the tasks like in the above figure have high degrees of freedom. As a result, degeneration is more frequent in these tasks, and in fact, they are the focus of this paper. | |||
The goal of the open-ended tasks is to generate the next n continuation tokens given a context sequence with m tokens. That is to maximize the following probability. | |||
==Likelihood Evaluation== | ==Likelihood Evaluation== | ||
Revision as of 17:40, 7 November 2020
Presented by
Donya Hamzeian
Introduction
Text generation is the act of automatically generating natural language texts like summarization, neural machine translation, fake news generation and etc. Degeneration happens when the output text is incoherent or produces repetitive results. For example in the figure below, the GPT2 model tries to generate the continuation text given the context. On the left side, the beam-search was used as the decoding strategy which has obviously stuck in a repetitive loop. On the right side, however, you can see how the pure sampling decoding strategy has generated incoherent results.
The authors argue that decoding strategies that are based on maximization like beam search lead to degeneration even with powerful models like GPT-2. Even though there are some utility functions that encourage diversity, they are not enough and the text generated by maximization, beam-search, or top-k sampling is too probable which indicates the lack of diversity (variance) compared to human-generated texts
Some may raise this question that the problem with beam-search may be due to search error i.e. they are more probable phrases that beam search is unable to find, but the point is that natural language has lower per-token probability on average and people usually optimize against saying the obvious.
The authors blame the long, unreliable tail in the probability distribution of tokens that the model samples from i.e. vocabularies with low probability frequently appear in the output text. So, top-k sampling with high values of k may produce texts closer to human texts, yet they have high variance in likelihood leading to incoherency issues. Therefore, instead of fixed k, it is good to dynamically increase or decrease the number of candidate tokens. Nucleus Sampling which is the contribution of this paper does this expansion and contraction of the candidate pool.
Language Model Decoding
There are two types of generation tasks. 1. Directed tasks: In these tasks, there are pairs of (input, output) that the model tries to generate the output text which is tightly scoped by the input text. Because of this constraint, these tasks less suffer from the degeneration. Summarization, neural machine translation, and input-to-text generation are some examples of these tasks.
2. Open-ended tasks like conditional story generation or the tasks like in the above figure have high degrees of freedom. As a result, degeneration is more frequent in these tasks, and in fact, they are the focus of this paper.
The goal of the open-ended tasks is to generate the next n continuation tokens given a context sequence with m tokens. That is to maximize the following probability.