  The rapid progress in Large Language Models (LLMs) could transform many
fields, but their fast development creates significant challenges for
oversight, ethical creation, and building user trust. This comprehensive review
looks at key trust issues in LLMs, such as unintended harms, lack of
transparency, vulnerability to attacks, alignment with human values, and
environmental impact. Many obstacles can undermine user trust, including
societal biases, opaque decision-making, potential for misuse, and the
challenges of rapidly evolving technology. Addressing these trust gaps is
critical as LLMs become more common in sensitive areas like finance,
healthcare, education, and policy. To tackle these issues, we suggest combining
ethical oversight, industry accountability, regulation, and public involvement.
AI development norms should be reshaped, incentives aligned, and ethics
integrated throughout the machine learning process, which requires close
collaboration across technology, ethics, law, policy, and other fields. Our
review contributes a robust framework to assess trust in LLMs and analyzes the
complex trust dynamics in depth. We provide contextualized guidelines and
standards for responsibly developing and deploying these powerful AI systems.
This review identifies key limitations and challenges in creating trustworthy
AI. By addressing these issues, we aim to build a transparent, accountable AI
ecosystem that benefits society while minimizing risks. Our findings provide
valuable guidance for researchers, policymakers, and industry leaders striving
to establish trust in LLMs and ensure they are used responsibly across various
applications for the good of society.
