3 papers
cs.CL2026
Bridging Traditional Explainability Methods and Multimodal Multilingual Models: An XAI-Based Analysis
Paweł Pozorski, Jakub Muszyński, Maria Ganzha
Multimodal Large Language Models (MLLMs) effectively integrate text and audio to interpret context in complex interactive dialogues. However, the internal mechanisms by which heter…
cs.CL2026
mllm-shap: A Shapley Value Explainability Platform for Text-Audio Multimodal Large Language Models
Jakub Muszyński, Paweł Pozorski, Maria Ganzha
We introduce mllm-shap, an open-source Python framework designed to extend Shapley Value (SV) explainability from text-only Large Language Models to Multimodal LLMs (MLLMs) process…
cs.SD2026
SGPA: Spectrogram-Guided Phonetic Alignment for Feasible Shapley Value Explanations in Multimodal Large Language Models
Paweł Pozorski, Jakub Muszyński, Maria Ganzha
Explaining the behavior of end-to-end audio language models via Shapley value attribution is intractable under native tokenization: a typical utterance yields over encoder fr…