{"url":"/method/picard","slug":"picard","name":"PICARD","full_name":"Parsing Incrementally for Constrained Auto-Regressive Decoding","full_name_withheld":false,"description_markdown":null,"description_state":"absent","introduced_year":null,"introduced_by":{"title":"PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models","paper":"/paper/picard-parsing-incrementally-for-constrained","first_author":"Torsten Scholak","n_authors":3,"url_abs":null,"archive_paper_url":"https://paperswithcode.com/paper/picard-parsing-incrementally-for-constrained"},"source":{"url":"https://arxiv.org/abs/2109.05093v1","title":"PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models","url_on_a_paper_host":true},"code_snippet_url":null,"code_snippet_url_on_a_code_host":false,"categories":[{"area":"Natural Language Processing","area_id":"natural-language-processing","collection":"Code Generation Transformers","url":"/methods/category/code-generation-transformers","pwc_aliases":[]}],"n_papers_tagged":18,"archive_num_papers":18,"papers_newest_first":[{"paper":"/paper/learning-to-integrate-diffusion-odes-by","title":"Learning to Integrate Diffusion ODEs by Averaging the Derivatives","date":"2025-05-20","arxiv_id":"2505.14502","n_code_links":0,"syntology":{"ran":3,"of":6,"unverified":3,"pointer_only":6}},{"paper":"/paper/fastdup-a-scalable-duplicate-marking-tool","title":"FastDup: a scalable duplicate marking tool using speculation-and-test mechanism","date":"2025-05-09","arxiv_id":"2505.06127","n_code_links":1,"syntology":null},{"paper":null,"title":"PCM : Picard Consistency Model for Fast Parallel Sampling of Diffusion Models","date":"2025-03-25","arxiv_id":"2503.19731","n_code_links":0,"syntology":null},{"paper":null,"title":"Picard-KKT-hPINN: Enforcing Nonlinear Enthalpy Balances for Physically Consistent Neural Networks","date":"2025-01-29","arxiv_id":"2501.17782","n_code_links":0,"syntology":null},{"paper":null,"title":"Parallel simulation for sampling under isoperimetry and score-based diffusion models","date":"2024-12-10","arxiv_id":"2412.07435","n_code_links":0,"syntology":null},{"paper":null,"title":"Multilevel Picard approximations and deep neural networks with ReLU, leaky ReLU, and softplus activation overcome the curse of dimensionality when approximating semilinear parabolic partial differential equations in $L^p$-sense","date":"2024-09-30","arxiv_id":"2409.20431","n_code_links":0,"syntology":null},{"paper":null,"title":"Speeding up Policy Simulation in Supply Chain RL","date":"2024-06-04","arxiv_id":"2406.01939","n_code_links":0,"syntology":null},{"paper":null,"title":"Accelerating Diffusion Models with Parallel Sampling: Inference at Sub-Linear Time Complexity","date":"2024-05-24","arxiv_id":"2405.15986","n_code_links":0,"syntology":null},{"paper":null,"title":"An Explicit Scheme for Pathwise XVA Computations","date":"2024-01-24","arxiv_id":"2401.13314","n_code_links":0,"syntology":null},{"paper":null,"title":"Domain Adaptation of a State of the Art Text-to-SQL Model: Lessons Learned and Challenges Found","date":"2023-12-09","arxiv_id":"2312.05448","n_code_links":0,"syntology":null},{"paper":"/paper/dreampropeller-supercharge-text-to-3d","title":"DreamPropeller: Supercharge Text-to-3D Generation with Parallel Sampling","date":"2023-11-28","arxiv_id":"2311.17082","n_code_links":1,"syntology":{"ran":11,"of":17,"unverified":6,"pointer_only":0}},{"paper":"/paper/machine-learning-detects-terminal","title":"Machine learning detects terminal singularities","date":"2023-09-21","arxiv_id":null,"n_code_links":1,"syntology":null},{"paper":null,"title":"Neural Partial Differential Equations with Functional Convolution","date":"2023-03-10","arxiv_id":"2303.07194","n_code_links":0,"syntology":null},{"paper":"/paper/pac-prediction-sets-for-large-language-models","title":"PAC Prediction Sets for Large Language Models of Code","date":"2023-02-17","arxiv_id":"2302.08703","n_code_links":1,"syntology":null},{"paper":"/paper/a-framework-to-evaluate-independent-component","title":"A Framework to Evaluate Independent Component Analysis applied to EEG signal: testing on the Picard algorithm","date":"2022-10-16","arxiv_id":"2210.08409","n_code_links":1,"syntology":null},{"paper":null,"title":"Deep learning approximations for non-local nonlinear PDEs with Neumann boundary conditions","date":"2022-05-07","arxiv_id":"2205.03672","n_code_links":0,"syntology":null},{"paper":null,"title":"A Tutorial on Solution Properties of State Space Models of Dynamical Systems","date":"2022-04-12","arxiv_id":"2204.06104","n_code_links":0,"syntology":null},{"paper":"/paper/picard-parsing-incrementally-for-constrained","title":"PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models","date":"2021-09-10","arxiv_id":"2109.05093","n_code_links":3,"syntology":{"ran":4,"of":7,"unverified":3,"pointer_only":0}}],"papers_shown":18,"tasks":[{"task":null,"name":"GPU","papers":2},{"task":"/task/semantic-parsing","name":"Semantic Parsing","papers":2},{"task":"/task/text-to-sql","name":"Text to SQL","papers":2},{"task":"/task/text-to-sql","name":"Text-To-SQL","papers":2},{"task":"/task/3d-generation","name":"3D Generation","papers":1},{"task":"/task/code-generation","name":"Code Generation","papers":1},{"task":"/task/denoising","name":"Denoising","papers":1},{"task":"/task/dialogue-state-tracking","name":"Dialogue State Tracking","papers":1},{"task":"/task/domain-adaptation","name":"Domain Adaptation","papers":1},{"task":"/task/eeg-1","name":"EEG","papers":1},{"task":"/task/eeg","name":"Electroencephalogram (EEG)","papers":1},{"task":"/task/image-generation","name":"Image Generation","papers":1},{"task":"/task/language-modeling","name":"Language Modeling","papers":1},{"task":"/task/language-modelling","name":"Language Modelling","papers":1},{"task":"/task/prediction","name":"Prediction","papers":1},{"task":"/task/state-space-models","name":"State Space Models","papers":1},{"task":"/task/structured-prediction","name":"Structured Prediction","papers":1},{"task":"/task/text-to-3d","name":"Text to 3D","papers":1},{"task":"/task/translation","name":"Translation","papers":1},{"task":"/task/blind-source-separation","name":"blind source separation","papers":1}],"tasks_shown":20,"n_tasks":22,"usage_by_year":[{"year":"2021","papers":1},{"year":"2022","papers":3},{"year":"2023","papers":5},{"year":"2024","papers":5},{"year":"2025","papers":4}],"row_source":"methods_table","archive":{"source":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","archive_url":"https://paperswithcode.com/method/picard"},"syntology_read_at":"2026-09-24T18:15:14+00:00"}