> For the complete documentation index, see [llms.txt](https://www.mlcompendium.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://www.mlcompendium.com/natural-language-processing-nlp/decoding-algorithms-for-nlp.md). # Decoding Algorithms For NLP A generative model gives a probability for every next token, and something still has to pick one. The page goes from a visual overview of greedy decoding, beam search, and sampling, to Hugging Face's practical guide to generating text, and then to shorter explanations of greedy and beam search in seq2seq models. The same notes are in [Basics](/predictive-ml/audio-basics.md), [GPT](/generative-ai/large-language-models-llms.md), and [SEQ2SEQ SEQUENCE TO SEQUENCE](/natural-language-processing-nlp/neural-nlp.md#seq2seq-sequence-to-sequence). The overview to start with is (great): [greedy, beam search, pure sampling, top k sampling](https://medium.com/voice-tech-podcast/visualising-beam-search-and-other-decoding-algorithms-for-natural-language-generation-fbba7cba2c5b) visualises beam search and the other decoding algorithms for natural language generation, and it is great, by Katnoria. Once the options are clear, the next step is running them. (great) [How to generate text by HuggingFace](https://huggingface.co/blog/how-to-generate) shows how to use the different decoding methods for language generation with Transformers. Beam search is the method most worth understanding in detail. For what is [beam](https://angelina-yang.medium.com/what-is-beam-search-decoding-in-a-neural-machine-translation-model-adaab30c6579) what search, angelina yang explains beam search decoding in a neural machine translation model through example interview questions, with Dharti Dhami also credited in the note. [understanding greedy and beam](https://medium.com/@jessica_lopez/understanding-greedy-search-and-beam-search-98c1e3cd821d) is by Jessica lopez, on greedy search and beam search as the well-known algorithms behind generation tasks such as neural machine translation and automatic summarization. The same interview-style post is the pointer for [beam in seq2seq](https://angelina-yang.medium.com/what-is-beam-search-decoding-in-a-neural-machine-translation-model-adaab30c6579). --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com. ## Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation by asking a question. Perform an HTTP GET request on the following URL with the `ask` and `goal` query parameters: ``` GET https://www.mlcompendium.com/natural-language-processing-nlp/decoding-algorithms-for-nlp.md?ask=&goal= ``` `ask` is the immediate question: it should be specific, self-contained, and written in natural language. `goal` is what the user is ultimately trying to achieve, the reason they need the answer. Sharing it helps GitBook give you a better, more relevant answer. A goal is most helpful when it describes the outcome the user wants rather than restating the question. For example, with `ask=how do I create an API token`, a goal like `automate deployments from our CI pipeline` lets GitBook tailor the answer to that use case. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.