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NLLG Quarterly arXiv Report 09/23: What are the most influential current AI Papers? (2312.05688v1)

Published 9 Dec 2023 in cs.DL, cs.AI, cs.CL, cs.CV, cs.CY, and cs.LG

Abstract: AI has witnessed rapid growth, especially in the subfields NLP, Machine Learning (ML) and Computer Vision (CV). Keeping pace with this rapid progress poses a considerable challenge for researchers and professionals in the field. In this arXiv report, the second of its kind, which covers the period from January to September 2023, we aim to provide insights and analysis that help navigate these dynamic areas of AI. We accomplish this by 1) identifying the top-40 most cited papers from arXiv in the given period, comparing the current top-40 papers to the previous report, which covered the period January to June; 2) analyzing dataset characteristics and keyword popularity; 3) examining the global sectoral distribution of institutions to reveal differences in engagement across geographical areas. Our findings highlight the continued dominance of NLP: while only 16% of all submitted papers have NLP as primary category (more than 25% have CV and ML as primary category), 50% of the most cited papers have NLP as primary category, 90% of which target LLMs. Additionally, we show that i) the US dominates among both top-40 and top-9k papers, followed by China; ii) Europe clearly lags behind and is hardly represented in the top-40 most cited papers; iii) US industry is largely overrepresented in the top-40 most influential papers.

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Analysis of Influential AI Papers: An Examination of Trends and Contributions from the NLLG Quarterly arXiv Report

The paper "NLLG Quarterly arXiv Report 09/23" authored by Zhang et al. provides a rigorous analysis of the most influential AI papers from January to September 2023. The paper, which is based on arXiv submissions, offers a curated perspective on trends within subfields like NLP, Machine Learning (ML), and Computer Vision (CV). This report, an update from the mid-year analysis conducted in June 2023, offers several key insights into scholarly activity, trends, geographic distribution, and institutional contributions within AI.

Methodology and Data Collection

The authors employ a standardized methodology for data collection and analysis. They extracted papers from arXiv using categories such as cs.CL (Computation and Language), cs.LG (Machine Learning), cs.CV (Computer Vision and Pattern Recognition), and cs.AI (Artificial Intelligence). Papers from January to September 2023 were filtered based on these categories. Citation counts for these papers were fetched from Semantic Scholar, and these counts were normalized using z-scores to ensure fair comparison across different publication timelines.

The report builds on the methodology from the previous quarter, incorporating a new approach to compute stable z-scores by averaging z-scores computed across different week divisions. This robust statistical technique mitigates the dependency on specific week definitions, providing a more reliable measure of citation impact.

Key Findings

Dominance of NLP and LLM Topics

The analysis underscores the dominance of NLP and LLMs. While NLP papers constitute a smaller fraction of total submissions (16%), they hold a significant portion of the top-40 most cited papers (50%), with 90% of these centered on LLMs. This indicates a sustained interest and activity in NLP research, particularly in the development and evaluation of LLMs.

Geographic and Institutional Distribution

Geographically, the United States leads both in the volume of papers and citation impact, followed by China. This dominance is apparent in both academia and industry, although the industry's role is more pronounced. European contributions, by contrast, are sparse among the top-40 most cited papers, indicating a potential disparity in research output and impact.

The institutional analysis reveals that US-based companies such as Google, Meta, and OpenAI are primary contributors to the top-40 list. These organizations undertake significant independent research, demonstrated by high fractional scores, indicating substantial internal contributions without external collaborations. Conversely, academic institutions tend to engage more in collaborative research, reflecting a broader trend of cross-institutional partnerships.

Trends in AI Research

The keyword analysis highlights critical trends in AI research, with terms like "LLM," "GPT," "LLaMA," and "multimodality" showing increasing relevance. LLaMA models, for instance, have quickly risen in prominence following their introduction, challenging the long-standing dominance of ChatGPT-focused research.

This shift towards diverse LLM models and multimodality suggests a broadening of research horizons. There is an increasing exploration of applications beyond text generation, incorporating advancements in vision-language integration and other multimodal AI systems.

Implications and Future Directions

The findings have several implications for the AI research community. The robust growth in NLP and LLM-focused research underscores the necessity for continuous evaluation and benchmarking frameworks to assess these models' performance and ethical considerations. Additionally, the enhanced role of industry players signifies a collaborative potential that could further spur innovation through partnerships between academia and industry.

Going forward, the report suggests an ongoing need to monitor these dynamic trends. Regular updates and expanded datasets could provide even deeper insights into the evolving landscape of AI research, helping scholars and practitioners stay abreast of key developments and emerging topics.

In sum, the "NLLG Quarterly arXiv Report 09/23" offers a comprehensive view of influential AI research trends, highlighting the critical roles of NLP, LLMs, and multimodality. The report’s detailed analysis provides valuable perspectives on the contributions of various geographic regions and institutions, offering a roadmap for future research and collaborations in the rapidly evolving field of AI.

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Authors (8)
  1. Ran Zhang (89 papers)
  2. Aida Kostikova (5 papers)
  3. Christoph Leiter (13 papers)
  4. Jonas Belouadi (12 papers)
  5. Daniil Larionov (12 papers)
  6. Yanran Chen (12 papers)
  7. Vivian Fresen (3 papers)
  8. Steffen Eger (90 papers)
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