@inproceedings{angelov-etal-2025-difficulty,
    title = "Difficulty Estimation in Natural Language Tasks with Action Scores",
    author = "Angelov, Aleksandar  and
      Tashu, Tsegaye Misikir  and
      Valdenegro-Toro, Matias",
    editor = "Cao, Trista  and
      Das, Anubrata  and
      Kumarage, Tharindu  and
      Wan, Yixin  and
      Krishna, Satyapriya  and
      Mehrabi, Ninareh  and
      Dhamala, Jwala  and
      Ramakrishna, Anil  and
      Galystan, Aram  and
      Kumar, Anoop  and
      Gupta, Rahul  and
      Chang, Kai-Wei",
    booktitle = "Proceedings of the 5th Workshop on Trustworthy NLP (TrustNLP 2025)",
    month = may,
    year = "2025",
    address = "Albuquerque, New Mexico",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/fix-sig-urls/2025.trustnlp-main.24/",
    pages = "351--364",
    ISBN = "979-8-89176-233-6",
    abstract = "This study investigates the effectiveness of the action score, a metric originally developed for computer vision tasks, in estimating sample difficulty across various natural language processing (NLP) tasks. Using transformer-based models, the action score is applied to sentiment analysis, natural language inference, and abstractive text summarization. The results demonstrate that the action score can effectively identify challenging samples in sentiment analysis and natural language inference, often capturing difficult instances that are missed by more established metrics like entropy. However, the effectiveness of the action score appears to be task-dependent, as evidenced by its performance in the abstractive text summarization task, where it exhibits a nearly linear relationship with entropy. The findings suggest that the action score can provide valuable insights into the characteristics of challenging samples in NLP tasks, particularly in classification settings. However, its application should be carefully considered in the context of each specific task and in light of emerging research on the potential value of hard samples in machine learning."
}