The Role of ChatGPT in Data Science: How AI-Assisted Conversational Interfaces Are Revolutionizing the Field

Mohammad Fraiwan Natheer Khasawneh

29 April 2023

ChatGPT is a type of artificial intelligence language model that uses deep learning algorithms to generate human-like responses to text-based prompts. The introduction of the latest ChatGPT version in November of 2022 has caused shockwaves in the industrial and academic communities for its powerful capabilities, plethora of possible applications, and the great possibility for abuse. At the time of writing this work, several other language models (e.g., Google Bard and Meta LLaMA) just came out in an attempt to get a foothold in the vast possible market. These models have the ability to revolutionize the way we interact with computers and have potential applications in many fields, including education, software engineering, healthcare, and marketing. In this paper, we will discuss the possible applications, drawbacks, and research directions using advanced language Chatbots (e.g., ChatGPT) in each of these fields. We first start with a brief introduction and the development timeline of artificial intelligence based language models, then we go through possible applications of such models, after that we discuss the limitations and drawbacks of the current technological state of the art, and finally we point out future possible research directions.

Walid Hariri

27 Maret 2023

Large language models, pivotal in artificial intelligence, find diverse applications. ChatGPT (Chat Generative Pre-trained Transformer), an OpenAI creation, stands out as a widely adopted, powerful tool. It excels in chatbots, content generation, language translation, recommendations, and medical applications, due to its ability to generate human-like responses, comprehend natural language, and adapt contextually. Its versatility and accuracy make it a potent force in natural language processing (NLP). Despite successes, ChatGPT has limitations, including biased responses and potential reinforcement of harmful language patterns. This article offers a comprehensive overview of ChatGPT, detailing its applications, advantages, and limitations. It also describes the main advancements from GPT-3 to GPT-4 Omni, comparing them with other LLMs like LLaMA 3, Gemini and Deepseek. The paper underscores the ethical imperative when utilizing this robust tool in practical settings. Furthermore, it contributes to ongoing discussions on artificial intelligence's impact on vision and NLP domains, providing insights into prompt engineering techniques.

A. Gerli F. Taccone Michele Salvagno

25 Februari 2023

This paper discusses the use of Artificial Intelligence Chatbot in scientific writing. ChatGPT is a type of chatbot, developed by OpenAI, that uses the Generative Pre-trained Transformer (GPT) language model to understand and respond to natural language inputs. AI chatbot and ChatGPT in particular appear to be useful tools in scientific writing, assisting researchers and scientists in organizing material, generating an initial draft and/or in proofreading. There is no publication in the field of critical care medicine prepared using this approach; however, this will be a possibility in the next future. ChatGPT work should not be used as a replacement for human judgment and the output should always be reviewed by experts before being used in any critical decision-making or application. Moreover, several ethical issues arise about using these tools, such as the risk of plagiarism and inaccuracies, as well as a potential imbalance in its accessibility between high- and low-income countries, if the software becomes paying. For this reason, a consensus on how to regulate the use of chatbots in scientific writing will soon be required.

Linjun Zhang James Y. Zou Xinming Tu + 1 lainnya

6 Juli 2023

The rapid advances of large language models (LLMs), such as ChatGPT, are revolutionizing data science and statistics. These state-of-the-art tools can streamline complex processes. As a result, it reshapes the role of data scientists. We argue that LLMs are transforming the responsibilities of data scientists, shifting their focus from hands-on coding, data-wrangling and conducting standard analyses to assessing and managing analyses performed by these automated AIs. This evolution of roles is reminiscent of the transition from a software engineer to a product manager. We illustrate this transition with concrete data science case studies using LLMs in this paper. These developments necessitate a meaningful evolution in data science education. Pedagogy must now place greater emphasis on cultivating diverse skillsets among students, such as LLM-informed creativity, critical thinking, AI-guided programming. LLMs can also play a significant role in the classroom as interactive teaching and learning tools, contributing to personalized education. This paper discusses the opportunities, resources and open challenges for each of these directions. As with any transformative technology, integrating LLMs into education calls for careful consideration. While LLMs can perform repetitive tasks efficiently, it's crucial to remember that their role is to supplement human intelligence and creativity, not to replace it. Therefore, the new era of data science education should balance the benefits of LLMs while fostering complementary human expertise and innovations. In conclusion, the rise of LLMs heralds a transformative period for data science and its education. This paper seeks to shed light on the emerging trends, potential opportunities, and challenges accompanying this paradigm shift, hoping to spark further discourse and investigation into this exciting, uncharted territory.

Adalbert Gerald Soosai Raj Xinyi Ai Meenakshi Syamkumar + 2 lainnya

7 Maret 2024

ChatGPT is a conversational AI platform that can produce code to solve problems when provided with a natural language prompt. Prior work on similar AI models has shown that they perform well on typical intro-level Computer Science problems. However, little is known about the performance of such tools on Data Science (DS) problems. In this work, we assess the performance of ChatGPT on assignments from three DS courses with varying difficulty levels. First, we apply the raw assignment prompts provided to the students and find that ChatGPT performs well on assignments with dataset(s) descriptions and progressive question prompts, which divide the programming requirements into sub-problems. Then, we perform prompt engineering on the assignments for which ChatGPT had low performance. We find that the following prompt engineering techniques significantly increased ChatGPT's performance: breaking down abstract questions into steps, breaking down steps into multiple prompts, providing descriptions of the dataset(s), including algorithmic details, adding specific instructions to entice specific actions, and removing extraneous information. Finally, we discuss how our findings suggest potential changes to curriculum design of DS courses.