mlsanityData to 14 Oct 2026

Active Learning for Natural Language Generation

arXiv 2305.15040 ↗First posted 24 May 2023cs.CLcited by 3

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Abstract (arXiv)

The field of Natural Language Generation (NLG) suffers from a severe shortage of labeled data due to the extremely expensive and time-consuming process involved in manual annotation. A natural approach for coping with this problem is active learning (AL), a well-known machine learning technique for improving annotation efficiency by selectively choosing the most informative examples to label. However, while AL has been well-researched in the context of text classification, its application to NLG remains largely unexplored. In this paper, we present a first systematic study of active learning for NLG, considering a diverse set of tasks and multiple leading selection strategies, and harnessing a strong instruction-tuned model. Our results indicate that the performance of existing AL strategies is inconsistent, surpassing the baseline of random example selection in some cases but not in others. We highlight some notable differences between the classification and generation scenarios, and analyze the selection behaviors of existing AL strategies. Our findings motivate exploring novel approaches for applying AL to generation tasks.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 34 of its 62 references that are in the map. First posted on arXiv 24 May 2023.

Builds on

34 of 62 references.

References · 34

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22 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 1; 2026: 1.

3 papers.

  1. 2025DUAL: Diversity and Uncertainty Active Learning for Text Summarizationcited by 3
  2. 2024Experimental Design for Active Transductive Inference in Large Language Modelscited by 0
  3. 2026RAIDAL: Redundancy-Aware Information Density Active Learning for CTC-Based Continuous Sign Language Recognitioncited by 0