Natural Language Processing (NLP) has seen significant advancements in recent years, mainly due to the growing size and power of large language models (LLMs). These models have not only showcased remarkable performances but are also making significant strides in real-world applications. To better understand their working and predictive reasoning, significant research and investigation has been dedicated to the field of interpretability and analysis (IA) in NLP. The primary goal of this research is to improve the efficiency, resilience, and trustworthiness of LLMs for safer and more successful implementation.
However, the impact of IA’s research on the development and construction of new NLP models is often minimal as it fails to offer pragmatic insights, particularly in ways to enhance the models. A recent research paper sought to address this issue by conducting an in-depth, mixed-methods analysis of the historical and present-day impact of IA research on NLP, in hopes of steering the future course of the field.
In their course of study, the researchers carried out a bibliometric study of 185,384 publications from the two main NLP conferences, ACL and EMNLP, spanning from 2018 to 2023, and surveyed 138 NLP community members for their perspectives. The study coupled quantitative data with a qualitative analysis of 556 articles and survey responses.
The findings of the research suggest that NLP researchers, regardless of whether they are involved in IA research themselves, turn to the IA studies for their work. Both researchers and practitioners in NLP view IA studies as a relevant resource for their work, for various reasons. IA findings also heavily influence the suggestion and creation of many new, non-IA methods for different areas, even if IA findings themselves do not drive influential non-IA work.
The researchers point out that despite these work results, there’s scope of improvement within the field of NLP. They call for actionable recommendations, human-centered, interdisciplinary work, and standardized, rigorous techniques after identifying these critical components currently lacking in IA research, based on survey responses.
Importantly, the study acknowledges that the 138 responses obtained may not be representative of the entire NLP field. The researchers advise interpreting the results of their study considering this limitation.
Ultimately, the study demystifies the impact of interpretability and analysis research on NLP. It underscores the importance of such research, while also illuminating the areas that require more focus and improvement to ensure the field’s future growth and success. The researchers’ endeavour marks a crucial milestone in trying to understand the logic behind large language models and their predictions, thereby making them more effective, robust, and trustworthy for real-world applications.
