mlsanityData to 7 Oct 2026

Character-level Convolutional Networks for Text Classification

arXiv 1509.01626 ↗First posted 4 Sep 2015cs.LG cs.CLcited by 2,293

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Cited by 147 in LLM safety and security, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

This article offers an empirical exploration on the use of character-level convolutional networks (ConvNets) for text classification. We constructed several large-scale datasets to show that character-level convolutional networks could achieve state-of-the-art or competitive results. Comparisons are offered against traditional models such as bag of words, n-grams and their TFIDF variants, and deep learning models such as word-based ConvNets and recurrent neural networks.

Key facts. Cited by 2,293 papers in the map (data through 7 Oct 2026). First posted on arXiv 4 Sep 2015.

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Citing papers per year: 2021: 305; 2022: 362; 2023: 454; 2024: 461; 2025: 388; 2026: 323.

2,293 papers.

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  1. 2021Finetuned Language Models Are Zero-Shot Learnerscited by 3,747
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  11. 2022A Survey on In-context Learningcited by 717
  12. 2021ByT5: Towards a token-free future with pre-trained byte-to-byte modelscited by 504
38 more
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  5. 2022RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learningcited by 361
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  13. 2021Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworkscited by 278
  14. 2024A Survey on Vision-Language-Action Models for Embodied AIcited by 274
  15. 2023Sentiment Analysis in the Era of Large Language Models: A Reality Checkcited by 262
  16. 2021Escaping the Big Data Paradigm with Compact Transformerscited by 261
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  18. 2021Surface Form Competition: Why the Highest Probability Answer Isn't Always Rightcited by 256
  19. 2024Nomic Embed: Training a Reproducible Long Context Text Embeddercited by 255
  20. 2022Black-Box Tuning for Language-Model-as-a-Servicecited by 254
  21. 2021A Survey on Deep Semi-supervised Learningcited by 250
  22. 2026Meta-Harness: End-to-End Optimization of Model Harnessescited by 248
  23. 2023In-context Vectors: Making In Context Learning More Effective and Controllable Through Latent Space Steeringcited by 240
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  25. 2021SPoT: Better Frozen Model Adaptation through Soft Prompt Transfercited by 235
  26. 2023Label Words are Anchors: An Information Flow Perspective for Understanding In-Context Learningcited by 235
  27. 2021Gradient-based Adversarial Attacks against Text Transformerscited by 233
  28. 2021PPT: Pre-trained Prompt Tuning for Few-shot Learningcited by 232
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  30. 2023Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Biascited by 230
  31. 2021CANINE: Pre-training an Efficient Tokenization-Free Encoder for Language Representationcited by 229
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  35. 2022Demystifying Prompts in Language Models via Perplexity Estimationcited by 215
  36. 2023In-Context Unlearning: Language Models as Few Shot Unlearnerscited by 214
  37. 2026Qwen3-VL-Embedding and Qwen3-VL-Reranker: A Unified Framework for State-of-the-Art Multimodal Retrieval and Rankingcited by 211
  38. 2022Is GPT-3 a Good Data Annotator?cited by 208

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