Shapley Value Attribution

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3 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

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mllm-shap: A Shapley Value Explainability Platform for Text-Audio Multimodal Large Language Models
Apr 21, 2026Jakub Muszyński, Paweł Pozorski, Maria GanzhaMultimodal Large Language Models Shapley Value Attribution
Warsaw University of Technology, Warsaw, Poland
SAVOIR: Learning Social Savoir-Faire via Shapley-based Reward Attribution
Apr 21, 2026Xiachong Feng, Yi Jiang, Xiaocheng Feng +9Credit Assignment in RL Step-Level Credit Assignment in RL
The University of Hong Kong · Harbin Institute of Technology · Harbin Institute of Technology, Shenzhen
From Scoring to Explanations: Evaluating SHAP and LLM Rationales for Rubric-based Teaching Quality Assessment
Apr 18, 2026Ivo Bueno, Babette Bühler, Philipp Stark +5Educational Assessment Shapley Value Attribution
1Technical University of Munich · 2Munich Center for Machine Learning (MCML) · 3Lund University +3
Towards Rigorous Explainability by Feature Attribution
Apr 17, 2026Olivier Létoffé, Xuanxiang Huang, Joao Marques-SilvaFeature Attribution Explainable Artificial Intelligence
IRIT, University of Toulouse France · Nanyang Technological University, Singapore · ICREA & Univ. Lleida, Spain
MinShap: A Modified Shapley Value Approach for Feature Selection
Apr 16, 2026Chenghui Zheng, Garvesh RaskuttiShapley Value Attribution Feature Selection
Department of Statistics, University of Wisconsin - Madison, Madison, WI 53706
Shapley-based Data Valuation for LLM Alignment via Sequential Preference Optimization
Dec 12, 2025Mélissa Tamine, Otmane Sakhi, Benjamin Heymann +2LLM Alignment Shapley Value Attribution
Criteo AI lab, FairPlay joint team, France · CREST, ENSAE, Institut Polytechnique de Paris · Criteo AI Lab, France +1
Fair Document Valuation in LLM Summaries via Shapley Values
May 28, 2025Zikun Ye, Hema YoganarasimhanShapley Value Attribution Data Valuation
University of Washington
Explaining a probabilistic prediction on the simplex with Shapley compositions
Aug 2, 2024Paul-Gauthier Noé, Miquel Perelló-Nieto, Jean-François Bonastre +1Shapley Value Attribution
Laboratoire Informatique d’Avignon, Avignon Universit´e, France · University of Bristol, United Kingdom
Neural Dynamic Data Valuation via Stochastic State-Adjoint Trajectories
Apr 30, 2024Zhangyong Liang, Ji Zhang, Huanhuan GaoCooperative Game Theory Game Theory
National Center for Applied Mathematics Tianjin University Tianjin 300072, China · School of Mathematics, Physics and Computing University of Southern Queensland Toowoomba, Queensland 4350, Australia · School of Mechanical and Aerospace Engineering Jilin University Changchun 130025, China

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mllm-shap: A Shapley Value Explainability Platform for Text-Audio Multimodal Large Language Models

Jakub Muszyński, Paweł Pozorski, Maria Ganzha

Warsaw University of Technology, Warsaw, Poland

Multimodal Large Language Models Shapley Value Attribution Multimodal Model Interpretability

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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) processing joint text and audio inputs. While text-based attribution is well-studied, mllm-shap addresses three critical challenges unique to the multimodal regime: (1) Modality-aware coalition masking, which manages the interleaved processing of discrete text tokens and dense audio encoder frames. (2) Multi-turn conversation tracking, utilizing per-token metadata to maintain role and modality context. (3) Phonetic alignment-based token grouping, a novel technique that reduces the coalition space by 10x to 50x, rendering SV estimation computationally feasible for long-form audio. The platform implements five SV estimation strategies, including a Complementary Contributions (CC) estimator with Neyman-optimal allocation that demonstrates superior convergence over standard Monte Carlo baselines. mllm-shap is provided as a pip-installable package featuring an interactive web-based GUI for granular attribution visualization. To our knowledge, this is the first publicly available framework providing a complete, reproducible pipeline for SV-based explainability in text-audio MLLMs.

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