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

arXiv: https://arxiv.org/abs/2309.02524 · HTML: https://mlsanity.com/paper/2309.02524/

## 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 (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [A Watermark for Large Language Models](https://mlsanity.com/paper/2301.10226/) (2023)
- [A Categorical Archive of ChatGPT Failures](https://mlsanity.com/paper/2302.03494/) (2023)
- [An Analysis of the Automatic Bug Fixing Performance of ChatGPT](https://mlsanity.com/paper/2301.08653/) (2023)
- [Consistency Analysis of ChatGPT](https://mlsanity.com/paper/2303.06273/) (2023)
- [In ChatGPT We Trust? Measuring and Characterizing the Reliability of ChatGPT](https://mlsanity.com/paper/2304.08979/) (2023)
- [Choose Your Programming Copilot: A Comparison of the Program Synthesis Performance of GitHub Copilot and Genetic Programming](https://mlsanity.com/paper/2111.07875/) (2021)

## Built on by (most cited first, 9 of 9)

- [Large Language Models Suffer From Their Own Output: An Analysis of the Self-Consuming Training Loop](https://mlsanity.com/paper/2311.16822/) (2023)
- [Towards Theoretical Understandings of Self-Consuming Generative Models](https://mlsanity.com/paper/2402.11778/) (2024)
- [Exploring User Security and Privacy Attitudes and Concerns Toward the Use of General-Purpose LLM Chatbots for Mental Health](https://mlsanity.com/paper/2507.10695/) (2025)
- [Epistemological Fault Lines Between Human and Artificial Intelligence](https://mlsanity.com/paper/2512.19466/) (2025)
- [Private Yet Social: How LLM Chatbots Support and Challenge Eating Disorder Recovery](https://mlsanity.com/paper/2412.11656/) (2024)
- [Recursive Chain-of-Feedback Prevents Performance Degradation from Redundant Prompting](https://mlsanity.com/paper/2402.02648/) (2024)
- [Mind the Gap! Choice Independence in Using Multilingual LLMs for Persuasive Co-Writing Tasks in Different Languages](https://mlsanity.com/paper/2502.09532/) (2025)
- [Enhancing Ground-to-Aerial Image Matching for Visual Misinformation Detection Using Semantic Segmentation](https://mlsanity.com/paper/2502.06288/) (2025)
- ["Are You Sure?": An Empirical Study of Human Perception Vulnerability in LLM-Driven Agentic Systems](https://mlsanity.com/paper/2602.21127/) (2026)

## 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.