Let Me Explain!: Explainable NLP for Understanding Large Language Models

Abstract

Large Language Models (LLMs) have transformed natural language processing (NLP), but their billions of parameters make them opaque. This lack of transparency is especially problematic in high-risk areas such as healthcare, finance, and content moderation, where understanding model decisions is essential for responsible use. This dissertation focuses on Explainable NLP (XNLP), making NLP models transparent, as a fundamental requirement for trustworthy AI systems. The dissertation addresses four research questions. RQ1 asks how XNLP methods can be designed and applied to meet the unique demands of high-risk domains such as healthcare, finance, and social media moderation. RQ2 investigates how token-level explanation methods can provide transparency in text classification systems and reveal vulnerabilities to adversarial manipulation. RQ3 examines the extent to which annotator demographics influence labeling decisions and how content-driven XAI techniques compare to demographic persona prompting for LLM-based annotation. RQ4 explores how moral alignment in LLMs can be evaluated across cultures in a systematic and transparent manner. To address these questions, the dissertation employs token-level explanation methods such as SHAP and LIME across multiple tasks. It surveys XNLP applications across domains, identifying gaps between methodological research and practical deployment; develops a transparent sexism-detection pipeline that combines BERT-based classification with SHAP explanations so that content moderators can verify decisions at the word level; and applies explainability methods to AI-generated text detection, revealing that detectors often rely on superficial features and showing that token-replacement experiments can expose these vulnerabilities and improve robustness. The dissertation then shifts to human-centered evaluation and moral alignment. It finds that text content is the dominant factor in annotation decisions, far outweighing annotator demographics, and that content-focused SHAP explanations are more effective than demographic persona prompting for guiding LLM annotations. It evaluates how well LLMs capture moral attitudes across cultures, finding that instruction-tuned models achieve moderate alignment with human survey data but show a persistent Western-centric bias. Finally, it introduces the EvalMORAAL framework, which combines Chain-of-Thought (CoT) reasoning with LLM-as-judge peer review; explicit reasoning consistently improved alignment compared with implicit scoring, though a significant gap between Western and non-Western regions remains. Together, these contributions show that explainability methods can improve both the reliability and the transparency of NLP systems. The dissertation also acknowledges limitations, including a focus on classification tasks and mainly English text, and limited human evaluation. It outlines a vision where explanations are actively integrated into model training, creating a feedback loop between human evaluation and model improvement, and the persistent regional difference in moral alignment underscores that making AI systems transparent and fair is an ongoing effort that requires continued attention to cultural diversity.