{
  "source_file": "0930",
  "source_platform": "Medium",
  "author": "Riccardo Andreoni",
  "title_listed": "Beam Search: the Most Used Algorithm in Sequence Models",
  "keywords": "",
  "domain": "文本处理",
  "content_type": "深度技术解析",
  "is_paid_flag": "否",
  "url": "https://towardsdatascience.com/beam-search-the-most-used-algorithm-in-sequence-models-107d56b23556/",
  "publish_date": "2023-12-13",
  "corpus_stage": "",
  "notes": "",
  "id": "ebacd7d73c8f",
  "collected_at": "2026-10-02T03:09:16",
  "title": "Beam Search: the Most Used Algorithm in Sequence Models | Towards Data Science",
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It also helps in adverstising on the website.\n\n\n  * Cookie\nedgebucket\n  * Duration\nsession\n  * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n  * Cookie\ndid\n  * Duration\n1 year\n  * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Artificial Intelligence](https://towardsdatascience.com/category/artificial-intelligence/)\n# Beam Search: the Most Used Algorithm in Sequence Models\nLearn the working principles of the most famous algorithm for text translation and speech recognition.\n[Riccardo Andreoni](https://towardsdatascience.com/author/riccardo.andreoni/)\nDecember 13, 20236 min read\n![Beam Search allows to consider multiple streams of candidates. Image source: unsplash.com.](../images/000.jpeg)\nBeam Search allows to consider multiple streams of candidates. Image source: [unsplash.com](https://unsplash.com/photos/multi-colored-light-streaks-on-white-background-ylMP3TetKoQ).\nImagine you're an [AI](https://en.wikipedia.org/wiki/Artificial_intelligence) language model, like [ChatGPT](https://chat.openai.com/), completing a sentence. How do you choose the next word to make it not just **grammatically correct** but also **contextually relevant**? This is where [**Beam Search**](https://en.wikipedia.org/wiki/Beam_search#:~:text=In%20computer%20science%2C%20beam%20search,that%20reduces%20its%20memory%20requirements.) steps in.\nBy efficiently **exploring multiple possibilities** in parallel and maintaining top candidates at each step, Beam Search plays a crucial role in the task of predicting **subsequent elements**. Being an effective and powerful algorithm, it ensures output aligns with grammatical constraints and the context.\nTo understand Beam Search's impact, think about all the applications requiring precise sequence generation, like **language translation** , **text completion** , and **chatbots**. In all these applications, Beam Search plays a critical role.\nIn this article, I will introduce the theory and guide you through a practical step-by-step example of the Beam Search algorithm. I will also present several Beam Search variants, and detail all the pros and cons of this fundamental algorithm.\nLearn this step by step with the interactive [AI Engineer](https://roadmap.sh/ai-engineer), [Computer Science](https://roadmap.sh/computer-science) and [Machine Learning](https://roadmap.sh/machine-learning) roadmaps.\n## Understanding Beam Search\nImagine you need to **translate the following sentence** from Spanish to English:\n> Pablo estará en Nueva York la próxima semana.\nWe don't only want to obtain a correct translation, we want to obtain **the best one**. For a language model, the best output coincides with the **most probable one**.\nTo achieve this task, most [sequence-to-sequence](https://blog.keras.io/a-ten-minute-introduction-to-sequence-to-sequence-learning-in-keras.html) models employ Beam Search. It serves as an heuristic algorithm, systematically exploring multiple possibilities in parallel. At each step, a defined \"beam width\" maintains a fixed number of top candidates. This allows the algorithm to explore several candidates.\nThis approach **mimics decision-making** processes, with the model evaluating and selecting the most promising options.\n## Beam Search Step-by-Step\nConsider a standard sequence-to-sequence model, represented by the simple network below:\n![Image by the author.](../images/001.png)\nImage by the author.\nAt each timestep _t_ , the model outputs a single word, used to compose the final sequence. The final sequence will be nothing other than the provided translated sentence.\nIf you are not familiar with RNNs, I suggest to check the simple introduction I included in this article:\n> [**Use Deep Learning to Generate Fantasy Names: Build a Language Model from Scratch**](https://towardsdatascience.com/use-deep-learning-to-generate-fantasy-character-names-build-a-language-model-from-scratch-792b13629efa)\nFor text translation, we commonly use an [**encoder**](https://en.wikipedia.org/wiki/Autoencoder), which is a portion of the network dedicated to converting the input sequence into a vectorial form. In this case, the input sequence is the Spanish sentence to be translated. The encoder is followed by a [**decoder**](https://towardsdatascience.com/understanding-encoder-decoder-sequence-to-sequence-model-679e04af4346) which, at each timestep, returns a piece of the output sequence.\n![Image by the author.](../images/002.png)\nImage by the author.\nLet's check the first step out in detail.\nThe encoder (green part) receives the Spanish sentence \"_Pablo estará en Nueva York la próxima semana._ \" and provides a vector form of it to the decoder (blue part).\n![Image by the author.](../images/003.png)\nImage by the author.\nAt timestep 1, the decoder will output the first word of the translation. The key question is:\n> How does the decoder choose the word?\nThe decoder chooses the most probable word, given the input sequence _x_. In other words, for each word in the dictionary, the model computes the corresponding probability to be the first word of the output sequence.\n![](../images/body-0e2bf15290eb584a854f.png)\nThe one word that maximizes this probability is chosen.\nHere is where **Beam Search enters the game** : it introduces a parameter _B_ , called **beam width** , which represents the number of words chosen by the model at each step.\nBy setting, for instance, _B=3_ , the model will return not only one but three candidate words.\nLet's suppose the most probable first words of the translation are \"_Pablo_ \", \"_Next_ \", and \"_During_ \".\n![Image by the author.](../images/004.png)\nImage by the author.\nFor each of these 3 candidate words, the model proceeds by guessing the second word _y²_ in the English translation.\n![Image by the author.](../images/005.png)\nImage by the author.\nAs always, the model picks the words that maximize a given probability. In this case:\n![](../images/body-1d8b3d0c26e99025cf6c.png)\nYou can clearly see the reason for the name Beam Search now. Starting from a narrow set of 3 candidates, the algorithm will enlarge it at each step, considering the probabilities of all these combinations.\nThe output of step 2 consists of 9 candidate sequences: \"_Pablo will_ \", \"_Pablo is_ \", ..., \"_During next_ \", and \"_During one_ \". The model associates each one of them with a probability. By setting the parameter _B=3_ , the model will keep only the three candidate sequences with the higher probability.\nSuppose that the candidates with higher probabilities are \"_Pablo will_ \", \"_Pablo is_ \", and \"_Next week_ \". During the 3-rd step, the decoder will guess 3 subsequent word for each candidate.\n![Image by the author.](../images/006.png)\nImage by the author.\nThe same process is iterated all over again until the model predicts as the most likely word the **End of Sentence** token. The [EOS](https://forum.opennmt.net/t/end-and-start-tokens/4570) token states that the sequence reached its end and it is now considered complete.\n## Beam Search Variants\nBeam Search adaptability can be enhanced by several variants of the algorithm. They provide options that may be more or less adequate for diverse sequence generation tasks. The most common variants are:\n  * **Length-Normalized Beam Search** : It adjusts for sentence length disparities by normalizing the probability scores, mitigating biases towards shorter sequences.\n  * **Diverse Beam Search** : It introduces diversity in candidate selection to prevent the algorithm from converging prematurely, promoting exploration of a broader solution space.\n  * **Top-**_**k**_**Beam Search** : It restricts the number of candidates considered at each step to the top-_k_ most probable, offering computational efficiency without compromising performance.\n  * **Alpha-Diverse Beam Search** : It controls the trade-off between diversity and optimization by introducing an alpha parameter, enabling fine-tuning based on specific task requirements.\n  * **Nucleus Sampling** : It defines a probability threshold (nucleus) and samples from the set of most probable candidates, enhancing the algorithm's focus on high-quality sequences.\n\n\n![Image source: unsplash.com.](../images/007.jpeg)\nImage source: [unsplash.com](https://unsplash.com/photos/multicolored-light-passage-in-dark-area-d3Zu34NBg7A).\n## Conclusion\nIn summary, in this post we understood why Beam Search is the **most used algorithm** in the field of sequence generation, particularly in the context of **language translation** models, but also for **chatbot responses**. Its systematic exploration of multiple possibilities in parallel, guided by a defined beam width, enhances the precision and the contextualize of the generated sequences.\nI am confident that the step-by-step example I provided will make the abstract concept of Beam Search more concrete.\nAs I always do in my posts, I will now recap some pros and cons of Beam Search. The main advantages of this algorithm are:\n  * **Precision** : Beam Search excels in generating precise and contextually relevant sequences.\n  * **Flexibility** : Variants like Diverse Beam Search and Top-_k_ Beam Search offer adaptability to diverse task requirements.\n  * **Efficiency** : It efficiently explores a solution space, balancing computational complexity with performance.\n\n\nHowever, in Machine Learning there are **no free meals**. It is then important to clarify the drawbacks of each algorithm. In the case of Beam Search, I identified these possible issues:\n  * **Computational Cost** : The exhaustive exploration of possibilities can incur computational overhead.\n  * **Potential Convergence** : In standard configurations, Beam Search might converge prematurely to suboptimal solutions.\n\n\n![Image by the author.](../images/008.png)\nImage by the author.\nFinally, I invite you to explore the provided resources to get a deeper understanding of this algorithm.\n* * *\nIf you liked this story, consider following me to be notified of my upcoming projects and articles!\n## References\n  * [Deep Learning for Natural Language Processing, Brownlee, J. (2019)](https://oku.ozturkibrahim.com/docs_python/Deep_Learning_for_Natural_Language_Processing.pdf)\n  * [Long short-term memory, Hochreiter, S., & Schmidhuber, J. (1997)](https://ieeexplore.ieee.org/abstract/document/6795963/)\n  * [Neural Turing Machines, Graves, A., et al. (2014)](https://arxiv.org/abs/1410.5401)\n  * [A Diversity-Promoting Objective Function for Neural Conversation Models, Li, J., et al. (2016)](https://aclanthology.org/N16-1014)\n  * [Neural Machine Translation by Jointly Learning to Align and Translate, Bahdanau, D., et al. (2015)](https://arxiv.org/abs/1409.0473)\n\n\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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It also helps in adverstising on the website.\n\n\n  * Cookie\nedgebucket\n  * Duration\nsession\n  * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n  * Cookie\ndid\n  * Duration\n1 year\n  * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Artificial Intelligence](https://towardsdatascience.com/category/artificial-intelligence/)\n# Beam Search: the Most Used Algorithm in Sequence Models\nLearn the working principles of the most famous algorithm for text translation and speech recognition.\n[Riccardo Andreoni](https://towardsdatascience.com/author/riccardo.andreoni/)\nDecember 13, 20236 min read\n![Beam Search allows to consider multiple streams of candidates. Image source: unsplash.com.](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/12/1haasTnsxIRy0kGUymxLkCg-scaled.jpeg)\nBeam Search allows to consider multiple streams of candidates. Image source: [unsplash.com](https://unsplash.com/photos/multi-colored-light-streaks-on-white-background-ylMP3TetKoQ).\nImagine you're an [AI](https://en.wikipedia.org/wiki/Artificial_intelligence) language model, like [ChatGPT](https://chat.openai.com/), completing a sentence. How do you choose the next word to make it not just **grammatically correct** but also **contextually relevant**? This is where [**Beam Search**](https://en.wikipedia.org/wiki/Beam_search#:~:text=In%20computer%20science%2C%20beam%20search,that%20reduces%20its%20memory%20requirements.) steps in.\nBy efficiently **exploring multiple possibilities** in parallel and maintaining top candidates at each step, Beam Search plays a crucial role in the task of predicting **subsequent elements**. Being an effective and powerful algorithm, it ensures output aligns with grammatical constraints and the context.\nTo understand Beam Search's impact, think about all the applications requiring precise sequence generation, like **language translation** , **text completion** , and **chatbots**. In all these applications, Beam Search plays a critical role.\nIn this article, I will introduce the theory and guide you through a practical step-by-step example of the Beam Search algorithm. I will also present several Beam Search variants, and detail all the pros and cons of this fundamental algorithm.\nLearn this step by step with the interactive [AI Engineer](https://roadmap.sh/ai-engineer), [Computer Science](https://roadmap.sh/computer-science) and [Machine Learning](https://roadmap.sh/machine-learning) roadmaps.\n## Understanding Beam Search\nImagine you need to **translate the following sentence** from Spanish to English:\n> Pablo estará en Nueva York la próxima semana.\nWe don't only want to obtain a correct translation, we want to obtain **the best one**. For a language model, the best output coincides with the **most probable one**.\nTo achieve this task, most [sequence-to-sequence](https://blog.keras.io/a-ten-minute-introduction-to-sequence-to-sequence-learning-in-keras.html) models employ Beam Search. It serves as an heuristic algorithm, systematically exploring multiple possibilities in parallel. At each step, a defined \"beam width\" maintains a fixed number of top candidates. This allows the algorithm to explore several candidates.\nThis approach **mimics decision-making** processes, with the model evaluating and selecting the most promising options.\n## Beam Search Step-by-Step\nConsider a standard sequence-to-sequence model, represented by the simple network below:\n![Image by the author.](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/12/1J8Kad_0FljJMyOLD-ozPOg.png)\nImage by the author.\nAt each timestep _t_ , the model outputs a single word, used to compose the final sequence. The final sequence will be nothing other than the provided translated sentence.\nIf you are not familiar with RNNs, I suggest to check the simple introduction I included in this article:\n> [**Use Deep Learning to Generate Fantasy Names: Build a Language Model from Scratch**](https://towardsdatascience.com/use-deep-learning-to-generate-fantasy-character-names-build-a-language-model-from-scratch-792b13629efa)\nFor text translation, we commonly use an [**encoder**](https://en.wikipedia.org/wiki/Autoencoder), which is a portion of the network dedicated to converting the input sequence into a vectorial form. In this case, the input sequence is the Spanish sentence to be translated. The encoder is followed by a [**decoder**](https://towardsdatascience.com/understanding-encoder-decoder-sequence-to-sequence-model-679e04af4346) which, at each timestep, returns a piece of the output sequence.\n![Image by the author.](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/12/1zA8Gu-ecHz8gCzgtRrBo9A.png)\nImage by the author.\nLet's check the first step out in detail.\nThe encoder (green part) receives the Spanish sentence \"_Pablo estará en Nueva York la próxima semana._ \" and provides a vector form of it to the decoder (blue part).\n![Image by the author.](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/12/1VTDBtUlQC3gsuHXpZhHGNg.png)\nImage by the author.\nAt timestep 1, the decoder will output the first word of the translation. The key question is:\n> How does the decoder choose the word?\nThe decoder chooses the most probable word, given the input sequence _x_. In other words, for each word in the dictionary, the model computes the corresponding probability to be the first word of the output sequence.\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/12/17iX_3oy-qT3pOHuEskb-Rw.png)\nThe one word that maximizes this probability is chosen.\nHere is where **Beam Search enters the game** : it introduces a parameter _B_ , called **beam width** , which represents the number of words chosen by the model at each step.\nBy setting, for instance, _B=3_ , the model will return not only one but three candidate words.\nLet's suppose the most probable first words of the translation are \"_Pablo_ \", \"_Next_ \", and \"_During_ \".\n![Image by the author.](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/12/1Rgabvjr9yqpjTIjRu4dfSA.png)\nImage by the author.\nFor each of these 3 candidate words, the model proceeds by guessing the second word _y²_ in the English translation.\n![Image by the author.](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/12/1b3nhZdVdOYKKMFe17p1SzA.png)\nImage by the author.\nAs always, the model picks the words that maximize a given probability. In this case:\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/12/11ZNyq747kmc3KCSdowTFiw.png)\nYou can clearly see the reason for the name Beam Search now. Starting from a narrow set of 3 candidates, the algorithm will enlarge it at each step, considering the probabilities of all these combinations.\nThe output of step 2 consists of 9 candidate sequences: \"_Pablo will_ \", \"_Pablo is_ \", ..., \"_During next_ \", and \"_During one_ \". The model associates each one of them with a probability. By setting the parameter _B=3_ , the model will keep only the three candidate sequences with the higher probability.\nSuppose that the candidates with higher probabilities are \"_Pablo will_ \", \"_Pablo is_ \", and \"_Next week_ \". During the 3-rd step, the decoder will guess 3 subsequent word for each candidate.\n![Image by the author.](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/12/1Lldkb04tNZpROoiUwjO0Kg.png)\nImage by the author.\nThe same process is iterated all over again until the model predicts as the most likely word the **End of Sentence** token. The [EOS](https://forum.opennmt.net/t/end-and-start-tokens/4570) token states that the sequence reached its end and it is now considered complete.\n## Beam Search Variants\nBeam Search adaptability can be enhanced by several variants of the algorithm. They provide options that may be more or less adequate for diverse sequence generation tasks. The most common variants are:\n  * **Length-Normalized Beam Search** : It adjusts for sentence length disparities by normalizing the probability scores, mitigating biases towards shorter sequences.\n  * **Diverse Beam Search** : It introduces diversity in candidate selection to prevent the algorithm from converging prematurely, promoting exploration of a broader solution space.\n  * **Top-**_**k**_**Beam Search** : It restricts the number of candidates considered at each step to the top-_k_ most probable, offering computational efficiency without compromising performance.\n  * **Alpha-Diverse Beam Search** : It controls the trade-off between diversity and optimization by introducing an alpha parameter, enabling fine-tuning based on specific task requirements.\n  * **Nucleus Sampling** : It defines a probability threshold (nucleus) and samples from the set of most probable candidates, enhancing the algorithm's focus on high-quality sequences.\n\n\n![Image source: unsplash.com.](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/12/1wbT3LZiXeP32TDH7TmWMww.jpeg)\nImage source: [unsplash.com](https://unsplash.com/photos/multicolored-light-passage-in-dark-area-d3Zu34NBg7A).\n## Conclusion\nIn summary, in this post we understood why Beam Search is the **most used algorithm** in the field of sequence generation, particularly in the context of **language translation** models, but also for **chatbot responses**. Its systematic exploration of multiple possibilities in parallel, guided by a defined beam width, enhances the precision and the contextualize of the generated sequences.\nI am confident that the step-by-step example I provided will make the abstract concept of Beam Search more concrete.\nAs I always do in my posts, I will now recap some pros and cons of Beam Search. The main advantages of this algorithm are:\n  * **Precision** : Beam Search excels in generating precise and contextually relevant sequences.\n  * **Flexibility** : Variants like Diverse Beam Search and Top-_k_ Beam Search offer adaptability to diverse task requirements.\n  * **Efficiency** : It efficiently explores a solution space, balancing computational complexity with performance.\n\n\nHowever, in Machine Learning there are **no free meals**. It is then important to clarify the drawbacks of each algorithm. In the case of Beam Search, I identified these possible issues:\n  * **Computational Cost** : The exhaustive exploration of possibilities can incur computational overhead.\n  * **Potential Convergence** : In standard configurations, Beam Search might converge prematurely to suboptimal solutions.\n\n\n![Image by the author.](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/12/1AlaaJbFBZ84nPCQiRznetQ.png)\nImage by the author.\nFinally, I invite you to explore the provided resources to get a deeper understanding of this algorithm.\n* * *\nIf you liked this story, consider following me to be notified of my upcoming projects and articles!\n## References\n  * [Deep Learning for Natural Language Processing, Brownlee, J. (2019)](https://oku.ozturkibrahim.com/docs_python/Deep_Learning_for_Natural_Language_Processing.pdf)\n  * [Long short-term memory, Hochreiter, S., & Schmidhuber, J. (1997)](https://ieeexplore.ieee.org/abstract/document/6795963/)\n  * [Neural Turing Machines, Graves, A., et al. (2014)](https://arxiv.org/abs/1410.5401)\n  * [A Diversity-Promoting Objective Function for Neural Conversation Models, Li, J., et al. (2016)](https://aclanthology.org/N16-1014)\n  * [Neural Machine Translation by Jointly Learning to Align and Translate, Bahdanau, D., et al. (2015)](https://arxiv.org/abs/1409.0473)\n\n\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. 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