mlsanityData to 14 Oct 2026

ATGen: A Framework for Active Text Generation

arXiv 2506.23342 ↗First posted 29 Jun 2025cs.CL cs.AIcited by 0

Not on the map. Why?

Abstract (arXiv)

Active learning (AL) has demonstrated remarkable potential in reducing the annotation effort required for training machine learning models. However, despite the surging popularity of natural language generation (NLG) tasks in recent years, the application of AL to NLG has been limited. In this paper, we introduce Active Text Generation (ATGen) - a comprehensive framework that bridges AL with text generation tasks, enabling the application of state-of-the-art AL strategies to NLG. Our framework simplifies AL-empowered annotation in NLG tasks using both human annotators and automatic annotation agents based on large language models (LLMs). The framework supports LLMs deployed as services, such as ChatGPT and Claude, or operated on-premises. Furthermore, ATGen provides a unified platform for smooth implementation and benchmarking of novel AL strategies tailored to NLG tasks. Finally, we present evaluation results for state-of-the-art AL strategies across diverse settings and multiple text generation tasks. We show that ATGen reduces both the effort of human annotators and costs associated with API calls to LLM-based annotation agents. The code of the framework is available on GitHub under the MIT license. The video presentation is available at http://atgen-video.nlpresearch.group

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 31 of its 58 references that are in the map. First posted on arXiv 29 Jun 2025.

Builds on

31 of 58 references.

References · 31

  1. 2021Training Verifiers to Solve Math Word Problemscited by 10,109
  2. 2023Efficient Memory Management for Large Language Model Serving with PagedAttentioncited by 5,944
  3. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
  4. 2019Parameter-Efficient Transfer Learning for NLPcited by 4,236
  5. 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,418
  6. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,955
  7. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,724
  8. 2023G-Eval: NLG Evaluation using GPT-4 with Better Human Alignmentcited by 2,276
  9. 2019HuggingFace's Transformers: State-of-the-art Natural Language Processingcited by 2,218
  10. 2023SGLang: Efficient Execution of Structured Language Model Programscited by 1,141
  11. 2017RACE: Large-scale ReAding Comprehension Dataset From Examinationscited by 861
  12. 2024DoRA: Weight-Decomposed Low-Rank Adaptationcited by 758
19 more
  1. 2021BARTScore: Evaluating Generated Text as Text Generationcited by 722
  2. 2021Datasets: A Community Library for Natural Language Processingcited by 530
  3. 2022Unnatural Instructions: Tuning Language Models with (Almost) No Human Laborcited by 397
  4. 2023AlignScore: Evaluating Factual Consistency with a Unified Alignment Functioncited by 288
  5. 2021Active Learning by Acquiring Contrastive Examplescited by 156
  6. 2020Cold-start Active Learning through Self-supervised Language Modelingcited by 128
  7. 2017Deep Active Learning for Named Entity Recognitioncited by 108
  8. 2021Revisiting Uncertainty-based Query Strategies for Active Learning with Transformerscited by 60
  9. 2019This Email Could Save Your Life: Introducing the Task of Email Subject Line Generationcited by 58
  10. 2021Active Learning for Sequence Tagging with Deep Pre-trained Models and Bayesian Uncertainty Estimatescited by 39
  11. 2020Wat zei je? Detecting Out-of-Distribution Translations with Variational Transformerscited by 31
  12. 2021Small-Text: Active Learning for Text Classification in Pythoncited by 19
  13. 2021Phrase-level Active Learning for Neural Machine Translationcited by 13
  14. 2021DeepAL: Deep Active Learning in Pythoncited by 13
  15. 2022FAMIE: A Fast Active Learning Framework for Multilingual Information Extractioncited by 8
  16. 2024To Label or Not to Label: Hybrid Active Learning for Neural Machine Translationcited by 8
  17. 2023Fabricator: An Open Source Toolkit for Generating Labeled Training Data with Teacher LLMscited by 7
  18. 2021Deep Active Learning for Text Classification with Diverse Interpretationscited by 5
  19. 2024Comparing the Efficacy of GPT-4 and Chat-GPT in Mental Health Care: A Blind Assessment of Large Language Models for Psychological Supportcited by 5

Built on by

Not cited yet.