Explainability for Large Language Models: A Survey

Analysis

Why This Paper Matters

As large language models (LLMs) become integral to real-world applications, their lack of transparency poses significant risks. This survey addresses a critical gap by systematically organizing explainability techniques tailored to LLMs, which differ fundamentally from traditional deep learning models due to their scale and emergent behaviors. The paper's taxonomy based on training paradigms (fine-tuning vs. prompting) is particularly timely, as prompting-based methods have become dominant with models like GPT-4 and LLaMA. By providing a structured overview, the paper helps practitioners navigate the rapidly growing field of LLM interpretability and choose appropriate methods for debugging, fairness auditing, and safety verification.

Technical Contributions

The paper's key innovation is its clear categorization of explainability techniques along two dimensions: training paradigm (fine-tuning vs. prompting) and explanation scope (local vs. global). For fine-tuning-based LLMs, it covers methods like attention analysis, probing classifiers, and feature attribution. For prompting-based LLMs, it discusses techniques such as chain-of-thought rationalization, concept-based explanations, and adversarial prompting. The survey also systematically reviews evaluation metrics for explanations (e.g., faithfulness, plausibility, stability) and demonstrates how explanations can be leveraged for model debugging and performance improvement. This structured framework enables researchers to identify gaps and compare approaches more effectively.

Results

As a survey, the paper does not report new experimental results. However, it synthesizes findings from over 100 cited works, highlighting that attention-based explanations remain the most common but often suffer from faithfulness issues. The survey notes that prompting-based methods, such as chain-of-thought, offer more human-interpretable explanations but may not always reflect true model reasoning. It also reports that evaluation metrics like comprehensiveness and sufficiency are increasingly used to quantify explanation quality, though no single metric is universally adopted.

Significance

This survey is highly significant for the AI community as it provides a foundational reference for LLM explainability at a time when regulatory pressure (e.g., EU AI Act) demands transparency. By distinguishing between fine-tuning and prompting paradigms, it helps practitioners select appropriate explanation methods for their specific deployment context. The paper also identifies open challenges, such as scaling explanations to very large models and ensuring explanations are robust to adversarial attacks, which will guide future research. For Neura Market's audience, this survey offers a practical roadmap for integrating explainability into LLM pipelines, enhancing trust and accountability in AI systems.