19 articles tagged with #shapley-values. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
ProMed introduces a reinforcement learning framework that transforms medical LLMs from reactive to proactive systems, using Shapley Information Gain to guide intelligent clinical questioning. The approach achieves 54.45% improvement over baseline reactive models and demonstrates strong generalization across medical benchmarks.
Researchers introduce SHAPE, a novel expert pruning framework for Sparse Mixture-of-Experts (MoE) language models that reduces memory requirements by up to 40% without retraining. Unlike traditional pruning methods that evaluate experts independently, SHAPE models expert cooperation using game theory, identifying which expert combinations matter most for model performance.
Researchers have proven that Shapley values, a key framework for attribution in machine learning, depend exclusively on the odd component of set functions. This theoretical breakthrough justifies the effectiveness of paired sampling and enables OddSHAP, a new estimator that achieves state-of-the-art accuracy by performing regression solely on the odd subspace using Fourier basis decomposition.
Researchers introduce ShaQ, a Shapley-value-based framework that identifies which specific parts of user input cause uncertainty in large language models, rather than just flagging overall uncertainty. The method achieves state-of-the-art ambiguity detection on multiple benchmarks and demonstrates practical value in high-stakes domains like clinical settings by enabling targeted input clarification.
Researchers identify fundamental flaws in Local Shapley Values and LIME, two widely-used machine learning interpretation methods that fail to reliably detect locally important features. They propose R-LOCO, a new approach that bridges local and global explanations by segmenting input space into regions and applying global attribution methods within those regions for more faithful local attributions.
Researchers present novel algorithms for computing Asymmetric Shapley Values (ASV), a machine learning explainability method that integrates causal knowledge. The work demonstrates polynomial-time computation in contexts where standard SHAP is #P-hard, with specialized algorithms for tree-structured causal graphs and approximation techniques for general directed acyclic graphs.
Researchers developed a Shapley-value-based framework to quantify how adjectives steer Large Language Model outputs across architectures (GPT-4o-mini, Llama-3-70b, DeepSeek-R1, Phi-3, o3). The study reveals that steering effects are model-dependent, non-universal, and exhibit complex interaction patterns—larger models show unpredictable compositional behavior while smaller models respond more literally, challenging the viability of one-size-fits-all prompting strategies.
Researchers introduce CQD-SHAP, a framework that explains how neural models answer complex queries over incomplete knowledge graphs by computing the contribution of each query component using Shapley values from game theory. This approach addresses the black-box nature of existing complex query answering methods and demonstrates consistent effectiveness across multiple datasets.
Researchers introduce mllm-shap, an open-source framework that extends Shapley Value explainability techniques to multimodal large language models processing text and audio inputs simultaneously. The platform addresses three technical challenges unique to multimodal systems and implements five estimation strategies, with a novel phonetic alignment technique reducing computational complexity by 10-50x.
Researchers have developed a novel framework extending Shapley Values—a traditional explainability method—to multimodal large language models that process both text and audio. The work introduces computational optimizations and a preprocessing technique called Spectrogram-Guided Phonetic Alignment to make the analysis feasible, alongside an open-source tool for visualization, revealing that input modality significantly affects model attribution patterns.
Researchers introduce TN-SHAP-G, a machine learning framework that efficiently computes Shapley values—a key method for explaining AI model decisions—by leveraging graph structure in data. The approach uses tensor networks to create compact surrogates that scale to larger datasets where traditional methods become computationally infeasible.
FedMTFI is a novel federated learning architecture that combines multi-teacher knowledge distillation with feature importance analysis to improve model training across heterogeneous devices with non-uniformly distributed data. The approach clusters clients by hardware similarity and uses Shapley values to identify important features during model distillation, achieving better accuracy than traditional federated learning algorithms.
Researchers introduce PatentXAI, a framework using Shapley values and graph-conditioned Markov Blankets to fairly allocate patent valuations within complex products containing thousands of patents. The method scales computationally by restricting coalition analysis to relevant patent subsets, achieving sub-100 millisecond processing times while maintaining accuracy within 6.2% of Monte Carlo benchmarks.
Researchers propose a new framework that reinterprets data selection as a sequential decision-making problem rooted in dynamic programming, unifying existing methods like Data Shapley while revealing their limitations as myopic approximations. The work introduces a scalable bipartite graph-based approach that preserves submodular structure and demonstrates improvements on machine learning and LLM fine-tuning tasks.
Researchers compared five post-hoc explainability methods for interpreting deep learning models trained to detect Major Depressive Disorder from EEG data. While different attribution approaches showed partially overlapping patterns emphasizing frontal and temporal brain regions, the study reveals methodological assumptions significantly influence interpretability results, cautioning against treating findings as definitive clinical biomarkers.
Researchers introduce Owen-Shapley Policy Optimization (OSPO), a reinforcement learning algorithm that improves how language models learn from feedback by attributing credit to individual tokens rather than treating entire sequences as atomic units. The method addresses a fundamental training gap in generative AI systems used for recommendation tasks, showing measurable improvements on real e-commerce datasets.
Researchers introduce PKeX-Shapley, an algorithm that computes exact Shapley values for product-kernel machine learning models in quadratic time, eliminating the need for approximations. The method exploits the multiplicative structure of product kernels to achieve linear-time-per-feature attribution without sampling or density estimation, extending beyond predictive models to statistical discrepancy measures like MMD and HSIC.
A new research paper challenges the rigor of popular explainability methods in machine learning, particularly Shapley values and SHAP, arguing that non-symbolic approaches lack the mathematical foundation needed for high-stakes applications. The work advocates for symbolic methods as a more reliable alternative for determining feature importance in AI models.
Researchers introduce COLA, a framework that refines counterfactual explanations in AI models by using optimal transport theory and Shapley values to achieve the same prediction changes with 26-45% fewer feature modifications. The method works across different datasets and models to create more actionable and clearer AI explanations.
