# Calibrated Trust in Dealing with LLM Hallucinations: A Qualitative Study

arXiv: https://arxiv.org/abs/2512.09088 · HTML: https://mlsanity.com/paper/2512.09088/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 44 references that are in the map.
- First posted on arXiv 9 Dec 2025.

## Builds on (most cited first, 13 of 13)

- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://mlsanity.com/paper/2311.05232/) (2023)
- [Hallucination is Inevitable: An Innate Limitation of Large Language Models](https://mlsanity.com/paper/2401.11817/) (2024)
- [A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models](https://mlsanity.com/paper/2401.01313/) (2024)
- [What Large Language Models Know and What People Think They Know](https://mlsanity.com/paper/2401.13835/) (2024)
- [Can Large Language Models Faithfully Express Their Intrinsic Uncertainty in Words?](https://mlsanity.com/paper/2405.16908/) (2024)
- [Understanding the Role of Human Intuition on Reliance in Human-AI Decision-Making with Explanations](https://mlsanity.com/paper/2301.07255/) (2023)
- [Trust in AI: Progress, Challenges, and Future Directions](https://mlsanity.com/paper/2403.14680/) (2024)
- [Accuracy-Time Tradeoffs in AI-Assisted Decision Making under Time Pressure](https://mlsanity.com/paper/2306.07458/) (2023)
- [Large Language Models vs. Search Engines: Evaluating User Preferences Across Varied Information Retrieval Scenarios](https://mlsanity.com/paper/2401.05761/) (2024)
- [In AI We Trust? Factors That Influence Trustworthiness of AI-infused Decision-Making Processes](https://mlsanity.com/paper/1912.02675/) (2019)
- [More Similar Values, More Trust? -- the Effect of Value Similarity on Trust in Human-Agent Interaction](https://mlsanity.com/paper/2105.09222/) (2021)
- [Exploring ChatGPT for Next-generation Information Retrieval: Opportunities and Challenges](https://mlsanity.com/paper/2402.11203/) (2024)

## Built on by (most cited first, 2 of 2)

- [RAG Strategies for Natural Language-Based SQL Query and REST API Call Generation](https://mlsanity.com/paper/2602.07086/) (2026)
- [A paradox of AI fluency](https://mlsanity.com/paper/2604.25905/) (2026)

## Abstract (arXiv)

Hallucinations are outputs by Large Language Models (LLMs) that are factually incorrect yet appear plausible [1]. This paper investigates how such hallucinations influence users' trust in LLMs and users' interaction with LLMs. To explore this in everyday use, we conducted a qualitative study with 192 participants. Our findings show that hallucinations do not result in blanket mistrust but instead lead to context-sensitive trust calibration. Building on the calibrated trust model by Lee & See [2] and Afroogh et al.'s trust-related factors [3], we confirm expectancy [3], [4], prior experience [3], [4], [5], and user expertise & domain knowledge [3], [4] as userrelated (human) trust factors, and identify intuition as an additional factor relevant for hallucination detection. Additionally, we found that trust dynamics are further influenced by contextual factors, particularly perceived risk [3] and decision stakes [6]. Consequently, we validate the recursive trust calibration process proposed by Bl"obaum [7] and extend it by including intuition as a user-related trust factor. Based on these insights, we propose practical recommendations for responsible and reflective LLM use.