The primary focus of IA research in NLP is to develop a deeper understanding of the behavior and inner workings of NLP systems and methods, as well as to explain the predictions made by large language models12. This research aims to improve the efficiency, robustness, and trustworthiness of these models for successful deployment in real-world applications.
The identified gaps in current IA research include the need for unification, actionable recommendations, human-centered approaches, interdisciplinary work, and standardized, rigorous techniques. These gaps hinder the full potential of IA research in improving NLP models and their applications.
NLP researchers utilize IA study results by building on their findings and incorporating them into their own work. IA research provides valuable insights into the behavior and inner workings of NLP systems, which helps researchers understand model capabilities and limitations, improve model trustworthiness, and develop novel methods for various NLP tasks.