LEMON: Local Explanations via Modality-aware OptimizatioN
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2026
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| _version_ | 1866915770202587136 |
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| author | Qin, Yu Sloan, Phillip Santos-Rodriguez, Raul Mirmehdi, Majid Filho, Telmo de Menezes e Silva |
| author_facet | Qin, Yu Sloan, Phillip Santos-Rodriguez, Raul Mirmehdi, Majid Filho, Telmo de Menezes e Silva |
| contents | Multimodal models are ubiquitous, yet existing explainability methods are often single-modal, architecture-dependent, or too computationally expensive to run at scale. We introduce LEMON (Local Explanations via Modality-aware OptimizatioN), a model-agnostic framework for local explanations of multimodal predictions. LEMON fits a single modality-aware surrogate with group-structured sparsity to produce unified explanations that disentangle modality-level contributions and feature-level attributions. The approach treats the predictor as a black box and is computationally efficient, requiring relatively few forward passes while remaining faithful under repeated perturbations. We evaluate LEMON on vision-language question answering and a clinical prediction task with image, text, and tabular inputs, comparing against representative multimodal baselines. Across backbones, LEMON achieves competitive deletion-based faithfulness while reducing black-box evaluations by 35-67 times and runtime by 2-8 times compared to strong multimodal baselines. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2602_02786 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | LEMON: Local Explanations via Modality-aware OptimizatioN Qin, Yu Sloan, Phillip Santos-Rodriguez, Raul Mirmehdi, Majid Filho, Telmo de Menezes e Silva Machine Learning Multimodal models are ubiquitous, yet existing explainability methods are often single-modal, architecture-dependent, or too computationally expensive to run at scale. We introduce LEMON (Local Explanations via Modality-aware OptimizatioN), a model-agnostic framework for local explanations of multimodal predictions. LEMON fits a single modality-aware surrogate with group-structured sparsity to produce unified explanations that disentangle modality-level contributions and feature-level attributions. The approach treats the predictor as a black box and is computationally efficient, requiring relatively few forward passes while remaining faithful under repeated perturbations. We evaluate LEMON on vision-language question answering and a clinical prediction task with image, text, and tabular inputs, comparing against representative multimodal baselines. Across backbones, LEMON achieves competitive deletion-based faithfulness while reducing black-box evaluations by 35-67 times and runtime by 2-8 times compared to strong multimodal baselines. |
| title | LEMON: Local Explanations via Modality-aware OptimizatioN |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2602.02786 |