{"version":1,"type":"story","url":"https://digestai.news/story/weakly-supervised-framework-uses-llms-to-extract-dataset-mentions-in","json":"https://digestai.news/story/weakly-supervised-framework-uses-llms-to-extract-dataset-mentions-in.json","markdown":"https://digestai.news/story/weakly-supervised-framework-uses-llms-to-extract-dataset-mentions-in.md","slug":"weakly-supervised-framework-uses-llms-to-extract-dataset-mentions-in","headline":"How Much Labeled Data Do You Need for Text Classifiers?","summary":"The article explores the impact of labeled data on text classification accuracy, using a synthetic dataset of support tickets. The study found that while modern large language models (LLMs) require no training data and offer instant inference, traditional methods like TF-IDF still perform better with fewer examples. Accuracy improves significantly when starting from very low volumes but levels off quickly as more labeled data is added. Key findings include the importance of understanding where errors occur—whether due to data scarcity or structural issues—and how these affect real-world performance. The article concludes that teams should not default to LLMs without first measuring their available data and considering other factors like volume and latency costs.","keyPoints":["Text classification accuracy improves with more labeled examples, but the gains diminish quickly.","TF-IDF vectorization performs better than modern LLMs at low volumes of labeled data.","Structural errors in text remain even with increased training data."],"whyItMatters":"Understanding the impact of labeled data on text classification can help teams make more informed decisions about which models to use and how much they should invest in labeling efforts.","category":{"slug":"models","name":"Generative AI & Models","url":"https://digestai.news/category/models"},"entities":{"companies":[],"models":["TF-IDF","LLM"],"people":[]},"firstPublishedAt":"2026-09-14T04:00:00Z","updatedAt":"2026-09-15T11:00:01Z","sourceCount":2,"hasPrimarySource":true,"sources":[{"outlet":"Towards Data Science","title":"How Many Labeled Examples Does a Text Classifier Actually Need? I Measured It.","url":"https://towardsdatascience.com/how-many-labeled-examples-does-a-text-classifier-actually-need-i-measured-it","publishedAt":"2026-09-15T11:00:01Z","type":"newsletter","primary":false,"lead":true},{"outlet":"arXiv cs.CL","title":"Extracting Dataset Mentions in Forced Displacement and FCV Documents: A Weakly Supervised Framework with LLM-Based Label Refinement","url":"https://arxiv.org/abs/2609.12107","publishedAt":"2026-09-14T04:00:00Z","type":"primary","primary":true,"lead":false}],"sourceNotes":null,"discussions":[],"thread":null,"cite":{"text":"Digest AI, \"How Much Labeled Data Do You Need for Text Classifiers?\", 14 September 2026, https://digestai.news/story/weakly-supervised-framework-uses-llms-to-extract-dataset-mentions-in","publisher":"Digest AI","title":"How Much Labeled Data Do You Need for Text Classifiers?","datePublished":"2026-09-14T04:00:00Z","url":"https://digestai.news/story/weakly-supervised-framework-uses-llms-to-extract-dataset-mentions-in"},"generatedBy":"Written by Digest AI's editorial model from the linked sources; the sources are the record.","license":"Headlines, digests and key points are written by Digest AI and may be quoted with a link to the story page. Linked articles belong to their publishers. Terms: https://digestai.news/terms#reuse"}