Papers by Daniel Gareev
Local and Global Decoding in Text Generation (2024.findings-emnlp)
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| Challenge: | Text generation relies heavily on decoding algorithms that sample strings from a language model distribution. |
| Approach: | They propose to introduce globally-normalised versions of traditional decoding methods and propose an independent Metropolis-Hastings algorithm to approximate sampling from globally-averaged distributions without explicitly computing them. |
| Outcome: | The proposed method approximates the distributions without explicitly computing them. |