ChatGPT is a Remarkable Tool -- For Experts
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Abstract (arXiv)
This paper investigates the capabilities of ChatGPT as an automated assistant in diverse domains, including scientific writing, mathematics, education, programming, and healthcare. We explore the potential of ChatGPT to enhance productivity, streamline problem-solving processes, and improve writing style. Furthermore, we highlight the potential risks associated with excessive reliance on ChatGPT in these fields. These limitations encompass factors like incorrect and fictitious responses, inaccuracies in code, limited logical reasoning abilities, overconfidence, and critical ethical concerns of copyrights and privacy violation. We outline areas and objectives where ChatGPT proves beneficial, applications where it should be used judiciously, and scenarios where its reliability may be limited. In light of observed limitations, and given that the tool's fundamental errors may pose a special challenge for non-experts, ChatGPT should be used with a strategic methodology. By drawing from comprehensive experimental studies, we offer methods and flow charts for effectively using ChatGPT. Our recommendations emphasize iterative interaction with ChatGPT and independent verification of its outputs. Considering the importance of utilizing ChatGPT judiciously and with expertise, we recommend its usage for experts who are well-versed in the respective domains.
Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 20 of its 83 references that are in the map. First posted on arXiv 2 Jun 2023.
Builds on
20 of 83 references.
References · 20
- 2020Language Models are Few-Shot Learnerscited by 32,199
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- 2023The political ideology of conversational AI: Converging evidence on ChatGPT's pro-environmental, left-libertarian orientationcited by 183
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- 2023Should ChatGPT be Biased? Challenges and Risks of Bias in Large Language Modelscited by 144
- 2023An Analysis of the Automatic Bug Fixing Performance of ChatGPTcited by 108
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- 2023ChatGPT is not all you need. A State of the Art Review of large Generative AI modelscited by 85
- 2022How would Stance Detection Techniques Evolve after the Launch of ChatGPT?cited by 76
- 2023Is ChatGPT the Ultimate Programming Assistant -- How far is it?cited by 72
- 2023One Small Step for Generative AI, One Giant Leap for AGI: A Complete Survey on ChatGPT in AIGC Eracited by 55
- 2023Learning gain differences between ChatGPT and human tutor generated algebra hintscited by 42
- 2023ChatGPT Needs SPADE (Sustainability, PrivAcy, Digital divide, and Ethics) Evaluation: A Reviewcited by 28
- 2023Exploring ChatGPT's Ability to Rank Content: A Preliminary Study on Consistency with Human Preferencescited by 25
- 2023How Generative AI models such as ChatGPT can be (Mis)Used in SPC Practice, Education, and Research? An Exploratory Studycited by 16
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Citing papers per year: 2023: 3; 2024: 3; 2025: 7; 2026: 3.
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- 2024Flooding Spread of Manipulated Knowledge in LLM-Based Multi-Agent Communities10 Jul 2024
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- 2025LightThinker: Thinking Step-by-Step Compressioncited by 104
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- 2025LightThinker: Thinking Step-by-Step Compression21 Feb 2025
- 2025Irony Detection, Reasoning and Understanding in Zero-shot Learning28 Jan 2025
- 2023Large Language Models for Software Engineering: A Systematic Literature Reviewcited by 389
- 2024Mapping the Ethics of Generative AI: A Comprehensive Scoping Reviewcited by 19
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- 2025MotivGraph-SoIQ: Integrating Motivational Knowledge Graphs and Socratic Dialogue for Enhanced LLM Ideationcited by 4
- 2025DIDS: Domain Impact-aware Data Sampling for Large Language Model Trainingcited by 3
- 2026How Controllable Are Large Language Models? A Unified Evaluation across Behavioral Granularitiescited by 3
- 2026Illusions of Confidence? Diagnosing LLM Truthfulness via Neighborhood Consistencycited by 2
- 2023ChatGPT may excel in States Medical Licensing Examination but falters in basic Linear Algebracited by 1
- 2023ChatGPT & Mechanical Engineering: Examining performance on the FE Mechanical Engineering and Undergraduate Examscited by 0
- 2024ChatGPT in Linear Algebra: Strides Forward, Steps to Gocited by 0
- 2026Responsible AI for General-Purpose Systems: Overview, Challenges, and A Path Forwardcited by 0
- 2026How Controllable Are Large Language Models? A Unified Evaluation across Behavioral Granularities3 Mar 2026
- 2026Responsible AI for General-Purpose Systems: Overview, Challenges, and A Path Forward19 Jan 2026
- 2026Illusions of Confidence? Diagnosing LLM Truthfulness via Neighborhood Consistency9 Jan 2026
- 2025MotivGraph-SoIQ: Integrating Motivational Knowledge Graphs and Socratic Dialogue for Enhanced LLM Ideation26 Sep 2025
- 2025DIDS: Domain Impact-aware Data Sampling for Large Language Model Training17 Apr 2025
- 2025CaKE: Circuit-aware Editing Enables Generalizable Knowledge Learners20 Mar 2025
- 2024ChatGPT in Linear Algebra: Strides Forward, Steps to Go18 Feb 2024
- 2024Mapping the Ethics of Generative AI: A Comprehensive Scoping Review13 Feb 2024
- 2023ChatGPT & Mechanical Engineering: Examining performance on the FE Mechanical Engineering and Undergraduate Exams26 Sep 2023
- 2023Large Language Models for Software Engineering: A Systematic Literature Review21 Aug 2023
- 2023ChatGPT may excel in States Medical Licensing Examination but falters in basic Linear Algebra23 Jun 2023
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