mlsanityData to 7 Oct 2026

NLLG Quarterly arXiv Report 06/23: What are the most influential current AI Papers?

arXiv 2308.04889 ↗First posted 31 Jul 2023cs.CY cs.AI cs.CLcited by 3

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Abstract (arXiv)

The rapid growth of information in the field of Generative Artificial Intelligence (AI), particularly in the subfields of Natural Language Processing (NLP) and Machine Learning (ML), presents a significant challenge for researchers and practitioners to keep pace with the latest developments. To address the problem of information overload, this report by the Natural Language Learning Group at Bielefeld University focuses on identifying the most popular papers on arXiv, with a specific emphasis on NLP and ML. The objective is to offer a quick guide to the most relevant and widely discussed research, aiding both newcomers and established researchers in staying abreast of current trends. In particular, we compile a list of the 40 most popular papers based on normalized citation counts from the first half of 2023. We observe the dominance of papers related to Large Language Models (LLMs) and specifically ChatGPT during the first half of 2023, with the latter showing signs of declining popularity more recently, however. Further, NLP related papers are the most influential (around 60% of top papers) even though there are twice as many ML related papers in our data. Core issues investigated in the most heavily cited papers are: LLM efficiency, evaluation techniques, ethical considerations, embodied agents, and problem-solving with LLMs. Additionally, we examine the characteristics of top papers in comparison to others outside the top-40 list (noticing the top paper's focus on LLM related issues and higher number of co-authors) and analyze the citation distributions in our dataset, among others.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 29 of its 35 references that are in the map. First posted on arXiv 31 Jul 2023.

Builds on

29 of 35 references.

References · 29

  1. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  2. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,393
  3. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,072
  4. 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,355
  5. 2023Tree of Thoughts: Deliberate Problem Solving with Large Language Modelscited by 3,155
  6. 2023InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuningcited by 2,855
  7. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,579
  8. 2023A Survey of Large Language Modelscited by 2,537
  9. 2023Voyager: An Open-Ended Embodied Agent with Large Language Modelscited by 2,081
  10. 2023PaLM-E: An Embodied Multimodal Language Modelcited by 2,037
  11. 2023Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understandingcited by 1,461
  12. 2023HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Facecited by 1,173
17 more
  1. 2023Large Language Models are not Fair Evaluatorscited by 1,050
  2. 2023A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivitycited by 888
  3. 2023The RefinedWeb Dataset for Falcon LLM: Outperforming Curated Corpora with Web Data, and Web Data Onlycited by 755
  4. 2023Extracting Training Data from Diffusion Modelscited by 723
  5. 2023Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizescited by 710
  6. 2023DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvaturecited by 705
  7. 2023A Watermark for Large Language Modelscited by 667
  8. 2023How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detectioncited by 472
  9. 2023The False Promise of Imitating Proprietary LLMscited by 254
  10. 2023Can Large Language Models Transform Computational Social Science?cited by 245
  11. 2023ChatGPT: A Meta-Analysis after 2.5 Monthscited by 50
  12. 2021The Dangers of Underclaiming: Reasons for Caution When Reporting How NLP Systems Failcited by 38
  13. 2019Does My Rebuttal Matter? Insights from a Major NLP Conferencecited by 31
  14. 2019Predicting Research Trends From Arxivcited by 5
  15. 2023Surveying (Dis)Parities and Concerns of Compute Hungry NLP Researchcited by 5
  16. 2022Did AI get more negative recently?cited by 4
  17. 2013Prediction of highly cited paperscited by 3

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Cited by year

Citing papers per year: 2023: 1; 2024: 2.

3 papers.

  1. 2024NLLG Quarterly arXiv Report 09/24: What are the most influential current AI Papers?cited by 3
  2. 2023NLLG Quarterly arXiv Report 09/23: What are the most influential current AI Papers?cited by 0
  3. 2024Evaluating the Efficacy of Foundational Models: Advancing Benchmarking Practices to Enhance Fine-Tuning Decision-Makingcited by 0