Understanding the Isolation in NLP

In an era where information is abundant, research disciplines are becoming increasingly isolated, which poses significant challenges.

"Communication—the human connection—is essential for success." — Paul J. Meyer

"The need for connection is as vital as the need for air, water, and food." — Dean Ornish

The impact of a study area on real-world applications and our understanding of various phenomena serves as a gauge of its significance. Grasping the interconnections and trends among different fields offers multiple advantages. To foster innovation, we must identify effective practices, recognize challenges, and pinpoint areas for improvement. Discoveries in one domain can profoundly affect others. For instance, artificial intelligence has greatly benefited from insights gained in neuroscience, shaping the design of model architectures and facilitating innovations.

However, the ever-increasing volume of publications complicates the understanding of inter-field interactions, rendering meta-analyses more challenging. Some researchers have expressed concerns that modern science is becoming more incremental rather than groundbreaking.

Therefore, examining the influences between fields can unveil new intersections and discoveries. Yet, answering these questions is no simple task. Many researchers rely on article citations as a tool to navigate these complexities. While citations serve as a rough indicator, numerous factors can skew these numbers, and biases from authors can further complicate the narrative.

Natural Language Processing (NLP) has witnessed remarkable growth in recent years, particularly with the emergence of large language models (LLMs). These models not only represent technological marvels but also hold significant social implications. Consequently, it is essential to investigate what factors shape this research area and what influences it in turn.

A recent study aimed to address these inquiries. Researchers amassed a dataset of 77,000 NLP articles, comprising 3.1 million citations and 1.8 million references to NLP papers. Their analysis sought to uncover:

• Which fields shape NLP research?
• Which domains are influenced by NLP?
• How self-contained is NLP, in terms of relying on its own innovations versus external influences?
• How has NLP's evolution unfolded over time?

The authors' dataset collection presented challenges, particularly in assigning accurate labels to articles. The definitions of fields can be ambiguous and evolve, so they turned to metadata from conferences and journals while also creating their own ontologies for improved classification.

The findings indicated that NLP receives fewer citations from other fields while citing them more frequently. As expected, NLP primarily cites computer science (81.8%), with the majority of incoming citations originating from this discipline (79.4%). Other notable fields include linguistics (7.6%), mathematics (2.8%), psychology (2.6%), and sociology (1.0%). The prominence of social sciences in the citation landscape is noteworthy.

The authors also explored trends, revealing that the percentage of citations to and from computer science has risen significantly over the years, indicating a shift towards a more computer science-centric NLP. Conversely, citations from linguistics and social sciences have diminished, while interest in mathematics and psychology has surged.

This transformation likely reflects advancements in machine translation, where models have matured, shifting focus toward reasoning tasks more closely associated with scientific disciplines. There is also a growing fascination with psychological aspects in relation to LLMs.

The authors introduced a Citation Field Diversity Index (CFDI) to assess how NLP is influenced by and influences other fields over time. Their analysis showed that while the average outgoing CFDI has gradually increased, NLP's own CFDI has experienced a sharp decline over the past four decades.

This decline in both incoming and outgoing citations suggests a decrease in NLP's interactions with other fields, apart from computer science. However, it's important to consider that NLP is a relatively new discipline, currently experiencing a vibrant and expanding community. Additionally, the LLM revolution is in its early stages, meaning that NLP's broader impact is still developing.

Interestingly, the authors investigated how often NLP papers cite other NLP papers versus those from outside fields. They found a notable increase in intra-citations, more pronounced than in other domains. This trend may be due to NLP's youth and the rapid growth in its published literature.

The rise in internal citations within NLP indicates a growing insularity, potentially linked to its methodological specialization. The reasons behind this trend remain unclear.

The authors made their code publicly available, inviting further exploration of their findings. While citations are not a perfect measure of a field's influence, this study provides valuable insights. Additionally, it acknowledges that personal biases, such as conference selections and collaborations, can affect citation practices. One limitation is the lack of universally accepted definitions for fields of study.

This research underscores a prevalent issue across various domains: an increasing tendency towards hyperspecialization and incremental research. The sheer volume of articles being published makes it challenging to stay informed about significant developments, not just in NLP but across all fields.

As I discussed with Ignacio de Gregorio, remaining updated on the latest advancements in AI presents its own set of difficulties. The challenge arises not only from the influx of exceptional new articles but also from identifying those that are genuinely significant amid the noise.

If we aim to advance towards artificial general intelligence (AGI), a convergence of diverse knowledge areas is crucial. The isolation of NLP may lead to a focus on applied, incremental research, prioritizing performance on established benchmarks over groundbreaking discoveries.

What are your thoughts? Which fields do you believe are underrepresented in influencing NLP? What domains could significantly shape the future of NLP? Share your insights in the comments.

If you found this analysis intriguing, consider exploring my other articles. Feel free to connect with me on LinkedIn or check out my GitHub repository for resources on machine learning, artificial intelligence, and more.

Chapter 2: The Impact of Interdisciplinarity on NLP

The video titled "NLP Parts Integration Resolve Inner Conflicts" discusses the importance of integrative approaches in NLP research, emphasizing the need for collaboration across disciplines.