Explainability of Large Language Models: Opportunities and Challenges toward Generating Trustworthy Explanations
arXiv:2510.17256v2 Announce Type: replace Abstract: Large language models have exhibited impressive performance across a broad range of downstream tasks in natural language processing. However, how a language model predicts the next token and generates content is not generally understandable by humans. Furthermore, these models often make errors in prediction and reasoning, known as hallucinations. These errors underscore the urgent need to better understand and interpret the intricate inner workings of language models and how they generate predictive outputs. Motivated by this gap, this paper
The rapid deployment and increasing reliance on large language models, despite their known issues with interpretability and 'hallucinations,' necessitates a deeper understanding for widespread adoption and trust.
A strategic reader should care because explainability is critical for overcoming adoption barriers in sensitive applications, ensuring regulatory compliance, and building public trust in AI systems.
This research highlights the shift from purely performance-driven AI development to one that equally prioritizes transparency and interpretability, impacting future model design and deployment strategies.
- · AI ethics researchers
- · Regulatory bodies
- · Enterprises adopting AI for critical functions
- · Explainable AI (XAI) solution providers
- · Developers of 'black box' AI
- · Organizations deploying unexplainable AI in sensitive areas
- · Users distrustful of AI
Increased focus and investment in research and development for explainable AI techniques will likely follow.
New industry standards and regulatory requirements for AI transparency across various sectors will emerge, demanding greater interpretability from deployed models.
Public confidence in AI will gradually improve, enabling broader and more impactful integration of AI into critical societal functions, but also exposing new ethical dilemmas related to explained errors.
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