{"url":"/task/multimodal-text-prediction","name":"Multimodal Text Prediction","slug":"multimodal-text-prediction","description_markdown":"**Multimodal text prediction** is a type of natural language processing that involves predicting the next word or sequence of words in a sentence, given multiple modalities or types of input. In traditional text prediction, the prediction is based solely on the context of the sentence, such as the words that precede the target word. In multimodal text prediction, additional modalities, such as images, audio, or user behavior, are also used to inform the prediction.\r\n\r\nFor example, in a multimodal text prediction system for captioning images, the system may use both the content of the image and the words that have been typed so far to generate the next word in the caption. The image may provide additional context or information about the content of the caption, while the typed words may provide information about the style or tone of the caption.\r\n\r\nMultimodal text prediction can be achieved using a variety of techniques, including deep learning models and statistical models. These models can be trained on large datasets of text and multimodal inputs to learn the relationships between the different types of data and improve the accuracy of the predictions.\r\n\r\nMultimodal text prediction has many applications, including chatbots, virtual assistants, and predictive text input for mobile devices. By incorporating additional modalities into the prediction process, multimodal text prediction systems can provide more accurate and useful predictions, improving the overall user experience.","categories":[{"name":"Natural Language Processing","url":"/area/natural-language-processing"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"archive_url"},"counts":{"papers_tagged":1,"papers_with_code":1,"benchmarks":1,"benchmark_tables_in_archive":1,"benchmark_tables_shown":1,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":2,"subtasks":0,"parent_tasks":0},"benchmarks":[{"leaderboard":"/sota/multimodal-text-prediction-on-multisubs","slug":"multimodal-text-prediction-on-multisubs","dataset":"MultiSubs","dataset_url":"/dataset/multisubs","rows_in_archive":1,"metrics":["Accuracy","Word similarity"],"first_row_in_archive_order":{"model":"9-gram LM with back-off","paper_title":"MultiSubs: A Large-scale Multimodal and Multilingual Dataset","paper_url":"/paper/multisubs-a-large-scale-multimodal-and","paper_date":"2021-03-02","arxiv_id":"2103.01910","code_links":[{"title":"josiahwang/multisubs-eval","url":"https://github.com/josiahwang/multisubs-eval"}],"syntology":null}}],"datasets":[{"url":"/dataset/scigraphqa","name":"SciGraphQA","full_name":"","num_papers_in_archive":8},{"url":"/dataset/multisubs","name":"MultiSubs","full_name":"MultiSubs: A Large-scale Multimodal and Multilingual Dataset","num_papers_in_archive":4}],"subtasks":[],"parent_tasks":[],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":1,"of":1,"tagged_in_all":1,"items":[{"url":"/paper/multisubs-a-large-scale-multimodal-and","title":"MultiSubs: A Large-scale Multimodal and Multilingual Dataset","date":"2021-03-02","arxiv_id":"2103.01910","repositories_listed":1,"syntology":null}],"syntology_records":0,"syntology_note":"a paper without a record is not a recorded non-run: it may lack an arXiv id or simply be absent from the graph layer"},"description_links":{"kept":0,"unwrapped_to_text":0,"bare_urls_linked":0,"relative_images_dropped":0,"rule":"internal links are kept only when the target slug exists in the catalog"},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per-sample execution status on synthesized fixtures ('ran N of M samples'); not a correctness claim and not a ranking signal.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"}},"not_shown":{"libraries":"the archive has no per-task library table","trend_sparklines":"the Trend column of the benchmarks table was a rendered image; it is not in the archive","social_and_latest_sorts":"stars and social signals are not in the archive"}}