# Mapping Trustworthiness in Large Language Models: A Bibliometric Analysis Bridging Theory to Practice

arXiv: https://arxiv.org/abs/2503.04785 · HTML: https://mlsanity.com/paper/2503.04785/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 90 references that are in the map.
- First posted on arXiv 27 Feb 2025.

## Builds on (most cited first, 26 of 26)

- [A Survey on Evaluation of Large Language Models](https://mlsanity.com/paper/2307.03109/) (2023)
- [DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models](https://mlsanity.com/paper/2306.11698/) (2023)
- [Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributions](https://mlsanity.com/paper/2108.09293/) (2021)
- [Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment](https://mlsanity.com/paper/2308.05374/) (2023)
- [TrustLLM: Trustworthiness in Large Language Models](https://mlsanity.com/paper/2401.05561/) (2024)
- [Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey](https://mlsanity.com/paper/2006.11371/) (2020)
- [Do Users Write More Insecure Code with AI Assistants?](https://mlsanity.com/paper/2211.03622/) (2022)
- [The Ethics of AI Ethics -- An Evaluation of Guidelines](https://mlsanity.com/paper/1903.03425/) (2019)
- [Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions](https://mlsanity.com/paper/2310.19775/) (2023)
- [Combating Misinformation in the Age of LLMs: Opportunities and Challenges](https://mlsanity.com/paper/2311.05656/) (2023)
- [Practical and Ethical Challenges of Large Language Models in Education: A Systematic Scoping Review](https://mlsanity.com/paper/2303.13379/) (2023)
- [The relationship between trust in AI and trustworthy machine learning technologies](https://mlsanity.com/paper/1912.00782/) (2019)
- [Designing for Responsible Trust in AI Systems: A Communication Perspective](https://mlsanity.com/paper/2204.13828/) (2022)
- [Investigating and Designing for Trust in AI-powered Code Generation Tools](https://mlsanity.com/paper/2305.11248/) (2023)
- [Worldwide AI Ethics: a review of 200 guidelines and recommendations for AI governance](https://mlsanity.com/paper/2206.11922/) (2022)
- [Can Large Language Models Provide Security & Privacy Advice? Measuring the Ability of LLMs to Refute Misconceptions](https://mlsanity.com/paper/2310.02431/) (2023)
- [Building Trustworthy NeuroSymbolic AI Systems: Consistency, Reliability, Explainability, and Safety](https://mlsanity.com/paper/2312.06798/) (2023)
- [The Contestation of Tech Ethics: A Sociotechnical Approach to Technology Ethics in Practice](https://mlsanity.com/paper/2106.01784/) (2021)
- [An Investigation into Misuse of Java Security APIs by Large Language Models](https://mlsanity.com/paper/2404.03823/) (2024)
- [ECCOLA -- a Method for Implementing Ethically Aligned AI Systems](https://mlsanity.com/paper/2004.08377/) (2020)
- [Informing clinical assessment by contextualizing post-hoc explanations of risk prediction models in type-2 diabetes](https://mlsanity.com/paper/2302.05752/) (2023)
- [Evaluating the Effectiveness of XAI Techniques for Encoder-Based Language Models](https://mlsanity.com/paper/2501.15374/) (2025)
- [Towards Reliable and Factual Response Generation: Detecting Unanswerable Questions in Information-Seeking Conversations](https://mlsanity.com/paper/2401.11452/) (2024)
- [Multi-Layer Ranking with Large Language Models for News Source Recommendation](https://mlsanity.com/paper/2406.11745/) (2024)
- [The EU AI Act is a good start but falls short](https://mlsanity.com/paper/2411.08535/) (2024)
- [Guardrails versus Gatekeepers: Understanding Product Managers' Ethical Decision-Making in Generative AI](https://mlsanity.com/paper/2501.16531/) (2025)

## Built on by (most cited first, 1 of 1)

- [In Generative AI We (Dis)Trust? Computational Analysis of Trust and Distrust in Reddit Discussions](https://mlsanity.com/paper/2510.16173/) (2025)

## Abstract (arXiv)

The rapid proliferation of Large Language Models (LLMs) has raised significant trustworthiness and ethical concerns. Despite the widespread adoption of LLMs across domains, there is still no clear consensus on how to define and operationalise trustworthiness. This study aims to bridge the gap between theoretical discussion and practical implementation by analysing research trends, definitions of trustworthiness, and practical techniques. We conducted a bibliometric mapping analysis of 2,006 publications from Web of Science (2019-2025) using the Bibliometrix, and manually reviewed 68 papers. We found a shift from traditional AI ethics discussion to LLM trustworthiness frameworks. We identified 18 different definitions of trust/trustworthiness, with transparency, explainability and reliability emerging as the most common dimensions. We identified 20 strategies to enhance LLM trustworthiness, with fine-tuning and retrieval-augmented generation (RAG) being the most prominent. Most of the strategies are developer-driven and applied during the post-training phase. Several authors propose fragmented terminologies rather than unified frameworks, leading to the risks of "ethics washing," where ethical discourse is adopted without a genuine regulatory commitment. Our findings highlight: persistent gaps between theoretical taxonomies and practical implementation, the crucial role of the developer in operationalising trust, and call for standardised frameworks and stronger regulatory measures to enable trustworthy and ethical deployment of LLMs.