Large Language Models (LLMs) have become central to modern AI applications, from content generation to enterprise data analysis. Yet their rapid adoption has raised a critical question: How can we trust models we cannot fully understand? The article Explainability for Large Language Models: A Survey (Aday‑Delgado et al., 2023) provides a comprehensive exploration of the emerging field of LLM explainability. Its position explains that not only is it a research concern but also a prerequisite for operationalizing AI responsibly, particularly in high‑stakes or regulated environments.
The authors present a structured taxonomy of explainability techniques for LLMs, dividing the landscape into two major paradigms: fine‑tuning‑based and prompt‑based models. Within each paradigm, they further differentiate local explanations, which help users understand why a model made a specific decision, from global explanations, which reveal patterns in overall model behavior. Techniques reviewed include attention visualization, gradient‑based saliency, probing with auxiliary classifiers, counterfactual examples, and retrieval‑ or example‑based explanations. This taxonomy helps practitioners navigate a complex research space with clarity and purpose.
Beyond classification, the survey highlights practical guidance for practitioners. Some methods excel at improving user trust but may lack faithfulness to the underlying model mechanics, while others offer precise attributions but are computationally costly. The authors emphasize that explainability is not one‑size‑fits‑all: the right approach depends on whether the goal is transparency, debugging, compliance, or fairness. They also note that scalable and faithful explanation techniques remain an open research challenge, particularly for extremely large, prompt‑driven models common in industry.
For professionals in data governance and enterprise AI, this survey is more than an academic exercise. It underscores the growing expectation that organizations deploying LLMs will need to explain them to regulators, auditors, and business users alike. As tools for explainability mature, data leaders can leverage them to strengthen trust, improve adoption, and reduce operational risks. The authors conclude that the future of LLM deployment will increasingly depend on a balance between raw performance and human‑centered transparency (Aday‑Delgado et al., 2023).
Reference
Aday‑Delgado‑Soto, J., López de Vergara, J. E., González, I., Perdices, D., & de Pedro, L. (2023). Explainability for large language models: A survey. Proceedings of the ACM. https://doi.org/10.1145/3639372
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