{"total":21,"rows":[{"id":"2109.11377","title":"WRENCH: A Comprehensive Benchmark for Weak Supervision","created":"2021-09-23","cited_by_count":96,"area":null},{"id":"2202.05433","title":"A Survey on Programmatic Weak Supervision","created":"2022-02-11","cited_by_count":66,"area":null},{"id":"2208.05379","title":"Multi-task Active Learning for Pre-trained Transformer-based Models","created":"2022-08-10","cited_by_count":16,"area":null},{"id":"2203.09735","title":"PRBoost: Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learning","created":"2022-03-18","cited_by_count":14,"area":null},{"id":"2203.01382","title":"Nemo: Guiding and Contextualizing Weak Supervision for Interactive Data Programming","created":"2022-03-02","cited_by_count":10,"area":null},{"id":"2308.00401","title":"VideoPro: A Visual Analytics Approach for Interactive Video Programming","created":"2023-08-01","cited_by_count":7,"area":null},{"id":"2112.08808","title":"Simple Questions Generate Named Entity Recognition Datasets","created":"2021-12-16","cited_by_count":5,"area":null},{"id":"2210.05632","title":"SEE-Few: Seed, Expand and Entail for Few-shot Named Entity Recognition","created":"2022-10-11","cited_by_count":4,"area":null},{"id":"2210.05296","title":"Natural Language Processing for Cognitive Analysis of Emotions","created":"2022-10-11","cited_by_count":3,"area":null},{"id":"2502.12366","title":"ScriptoriumWS: A Code Generation Assistant for Weak Supervision","created":"2025-02-17","cited_by_count":3,"area":null},{"id":"2403.02932","title":"RulePrompt: Weakly Supervised Text Classification with Prompting PLMs and Self-Iterative Logical Rules","created":"2024-03-05","cited_by_count":2,"area":null},{"id":"2208.09625","title":"SPOT: Knowledge-Enhanced Language Representations for Information Extraction","created":"2022-08-20","cited_by_count":1,"area":null},{"id":"2210.02768","title":"Distilling Task-specific Logical Rules from Large Pre-trained Models","created":"2022-10-06","cited_by_count":1,"area":null},{"id":"2210.07586","title":"Automatic Creation of Named Entity Recognition Datasets by Querying Phrase Representations","created":"2022-10-14","cited_by_count":1,"area":null},{"id":"2308.06454","title":"Demonstration-based learning for few-shot biomedical named entity recognition under machine reading comprehension","created":"2023-08-12","cited_by_count":1,"area":null},{"id":"2403.11103","title":"ProgGen: Generating Named Entity Recognition Datasets Step-by-step with Self-Reflexive Large Language Models","created":"2024-03-17","cited_by_count":1,"area":null},{"id":"2407.07982","title":"Automating Weak Label Generation for Data Programming with Clinicians in the Loop","created":"2024-07-10","cited_by_count":1,"area":null},{"id":"2604.08578","title":"Structured Exploration and Exploitation of Label Functions for Automated Data Annotation","created":"2026-03-28","cited_by_count":1,"area":null},{"id":"2302.03297","title":"AutoWS: Automated Weak Supervision Framework for Text Classification","created":"2023-02-07","cited_by_count":0,"area":null},{"id":"2407.02719","title":"Boosting Biomedical Concept Extraction by Rule-Based Data Augmentation","created":"2024-07-03","cited_by_count":0,"area":null},{"id":"2410.07094","title":"An Approach for Auto Generation of Labeling Functions for Software Engineering Chatbots","created":"2024-10-09","cited_by_count":0,"area":null}],"next_cursor":null}