mlsanityData to 14 Oct 2026

Do You Trust ChatGPT? -- Perceived Credibility of Human and AI-Generated Content

arXiv 2309.02524 ↗First posted 5 Sep 2023cs.HC cs.AIcited by 9

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Abstract (arXiv)

This paper examines how individuals perceive the credibility of content originating from human authors versus content generated by large language models, like the GPT language model family that powers ChatGPT, in different user interface versions. Surprisingly, our results demonstrate that regardless of the user interface presentation, participants tend to attribute similar levels of credibility. While participants also do not report any different perceptions of competence and trustworthiness between human and AI-generated content, they rate AI-generated content as being clearer and more engaging. The findings from this study serve as a call for a more discerning approach to evaluating information sources, encouraging users to exercise caution and critical thinking when engaging with content generated by AI systems.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 10 of its 44 references that are in the map. First posted on arXiv 5 Sep 2023.

Builds on

10 of 44 references.

References · 10

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2022Training language models to follow instructions with human feedbackcited by 15,971
  4. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  5. 2023A Watermark for Large Language Modelscited by 725
  6. 2023A Categorical Archive of ChatGPT Failurescited by 168
  7. 2023An Analysis of the Automatic Bug Fixing Performance of ChatGPTcited by 110
  8. 2023Consistency Analysis of ChatGPTcited by 37
  9. 2023In ChatGPT We Trust? Measuring and Characterizing the Reliability of ChatGPTcited by 37
  10. 2021Choose Your Programming Copilot: A Comparison of the Program Synthesis Performance of GitHub Copilot and Genetic Programmingcited by 24

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 3; 2025: 4; 2026: 1.

9 papers.

  1. 2023Large Language Models Suffer From Their Own Output: An Analysis of the Self-Consuming Training Loopcited by 79
  2. 2024Towards Theoretical Understandings of Self-Consuming Generative Modelscited by 29
  3. 2025Exploring User Security and Privacy Attitudes and Concerns Toward the Use of General-Purpose LLM Chatbots for Mental Healthcited by 13
  4. 2025Epistemological Fault Lines Between Human and Artificial Intelligencecited by 11
  5. 2024Private Yet Social: How LLM Chatbots Support and Challenge Eating Disorder Recoverycited by 8
  6. 2024Recursive Chain-of-Feedback Prevents Performance Degradation from Redundant Promptingcited by 3
  7. 2025Mind the Gap! Choice Independence in Using Multilingual LLMs for Persuasive Co-Writing Tasks in Different Languagescited by 3
  8. 2025Enhancing Ground-to-Aerial Image Matching for Visual Misinformation Detection Using Semantic Segmentationcited by 1
  9. 2026"Are You Sure?": An Empirical Study of Human Perception Vulnerability in LLM-Driven Agentic Systemscited by 0