3 papers
cs.CL2026
Learning to Explain: Supervised Token Attribution from Transformer Attention Patterns
George Mihaila
Explainable AI (XAI) has become critical as transformer-based models are deployed in high-stakes applications including healthcare, legal systems, and financial services, where opa…
cs.CL2026
LIME-LLM: Probing Models with Fluent Counterfactuals, Not Broken Text
George Mihaila, Suleyman Olcay Polat, Poli Nemkova +3
Local explanation methods such as LIME (Ribeiro et al., 2016) remain fundamental to trustworthy AI, yet their application to NLP is limited by a reliance on random token masking. T…
cs.CL2025
Tagging-Augmented Generation: Assisting Language Models in Finding Intricate Knowledge In Long Contexts
Anwesan Pal, Karen Hovsepian, Tinghao Guo +5
Recent investigations into effective context lengths of modern flagship large language models (LLMs) have revealed major limitations in effective question answering (QA) and reason…