# A Glimpse in ChatGPT Capabilities and its impact for AI research arXiv: https://arxiv.org/abs/2305.06087 ยท HTML: https://mlsanity.com/paper/2305.06087/ ## Key facts - Cited by 6 papers in the map (data through 14 Oct 2026). - Builds on 34 of its 73 references that are in the map. - First posted on arXiv 10 May 2023. ## Builds on (most cited first, 30 of 34) - [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017) - [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023) - [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022) - [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023) - [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022) - [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020) - [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022) - [Toolformer: Language Models Can Teach Themselves to Use Tools](https://mlsanity.com/paper/2302.04761/) (2023) - [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://mlsanity.com/paper/1909.08053/) (2019) - [LaMDA: Language Models for Dialog Applications](https://mlsanity.com/paper/2201.08239/) (2022) - [Diffusion Models: A Comprehensive Survey of Methods and Applications](https://mlsanity.com/paper/2209.00796/) (2022) - [A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity](https://mlsanity.com/paper/2302.04023/) (2023) - [Galactica: A Large Language Model for Science](https://mlsanity.com/paper/2211.09085/) (2022) - [Language Is Not All You Need: Aligning Perception with Language Models](https://mlsanity.com/paper/2302.14045/) (2023) - [Improving alignment of dialogue agents via targeted human judgements](https://mlsanity.com/paper/2209.14375/) (2022) - [Is ChatGPT a General-Purpose Natural Language Processing Task Solver?](https://mlsanity.com/paper/2302.06476/) (2023) - [Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task](https://mlsanity.com/paper/2210.13382/) (2022) - [Using cognitive psychology to understand GPT-3](https://mlsanity.com/paper/2206.14576/) (2022) - [Learning Latent Plans from Play](https://mlsanity.com/paper/1903.01973/) (2019) - [ChatGPT for Robotics: Design Principles and Model Abilities](https://mlsanity.com/paper/2306.17582/) (2023) - [BlenderBot 3: a deployed conversational agent that continually learns to responsibly engage](https://mlsanity.com/paper/2208.03188/) (2022) - [A Categorical Archive of ChatGPT Failures](https://mlsanity.com/paper/2302.03494/) (2023) - [Deep Learning Based Text Classification: A Comprehensive Review](https://mlsanity.com/paper/2004.03705/) (2020) - [What Matters in Language Conditioned Robotic Imitation Learning over Unstructured Data](https://mlsanity.com/paper/2204.06252/) (2022) - [Language-Conditioned Imitation Learning for Robot Manipulation Tasks](https://mlsanity.com/paper/2010.12083/) (2020) - [Translating Natural Language to Planning Goals with Large-Language Models](https://mlsanity.com/paper/2302.05128/) (2023) - ["Going on a vacation" takes longer than "Going for a walk": A Study of Temporal Commonsense Understanding](https://mlsanity.com/paper/1909.03065/) (2019) - [ChatGPT is not all you need. A State of the Art Review of large Generative AI models](https://mlsanity.com/paper/2301.04655/) (2023) - [Applying BERT and ChatGPT for Sentiment Analysis of Lyme Disease in Scientific Literature](https://mlsanity.com/paper/2302.06474/) (2023) - [Collaborative Storytelling with Large-scale Neural Language Models](https://mlsanity.com/paper/2011.10208/) (2020) ## Built on by (most cited first, 6 of 6) - [CoPAL: Corrective Planning of Robot Actions with Large Language Models](https://mlsanity.com/paper/2310.07263/) (2023) - [Analysis of ChatGPT on Source Code](https://mlsanity.com/paper/2306.00597/) (2023) - [Testing the Depth of ChatGPT's Comprehension via Cross-Modal Tasks Based on ASCII-Art: GPT3.5's Abilities in Regard to Recognizing and Generating ASCII-Art Are Not Totally Lacking](https://mlsanity.com/paper/2307.16806/) (2023) - [Large Language Models Understand Layout](https://mlsanity.com/paper/2407.05750/) (2024) - [Technical Language Processing for Telecommunications Specifications](https://mlsanity.com/paper/2406.02325/) (2024) - [Conversational AI for Rapid Scientific Prototyping: A Case Study on ESA's ELOPE Competition](https://mlsanity.com/paper/2601.04920/) (2026) ## Abstract (arXiv) Large language models (LLMs) have recently become a popular topic in the field of Artificial Intelligence (AI) research, with companies such as Google, Amazon, Facebook, Amazon, Tesla, and Apple (GAFA) investing heavily in their development. These models are trained on massive amounts of data and can be used for a wide range of tasks, including language translation, text generation, and question answering. However, the computational resources required to train and run these models are substantial, and the cost of hardware and electricity can be prohibitive for research labs that do not have the funding and resources of the GAFA. In this paper, we will examine the impact of LLMs on AI research. The pace at which such models are generated as well as the range of domains covered is an indication of the trend which not only the public but also the scientific community is currently experiencing. We give some examples on how to use such models in research by focusing on GPT3.5/ChatGPT3.4 and ChatGPT4 at the current state and show that such a range of capabilities in a single system is a strong sign of approaching general intelligence. Innovations integrating such models will also expand along the maturation of such AI systems and exhibit unforeseeable applications that will have important impacts on several aspects of our societies.