TabSHAP

• URL: http://arxiv.org/abs/2604.21120v1
• Date: Wed, 22 Apr 2026 22:04:45 GMT
• Title: TabSHAP
• Abstract summary: Large Language Models are emerging as powerful alternatives to traditional tree-based models.<n>Existing methods often rely on global linear proxies or scalar probability shifts that fail to capture the model's full probabilistic uncertainty.<n>We introduce TabSHAP, a model-agnostic interpretability framework designed to directly attribute local query decision logic.
• Score: 5.3259362673757735
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) fine-tuned on serialized tabular data are emerging as powerful alternatives to traditional tree-based models, particularly for heterogeneous or context-rich datasets. However, their deployment in high-stakes domains is hindered by a lack of faithful interpretability; existing methods often rely on global linear proxies or scalar probability shifts that fail to capture the model's full probabilistic uncertainty. In this work, we introduce TabSHAP, a model-agnostic interpretability framework designed to directly attribute local query decision logic in LLM-based tabular classifiers. By adapting a Shapley-style sampled-coalition estimator with Jensen-Shannon divergence between full-input and masked-input class distributions, TabSHAP quantifies the distributional impact of each feature rather than simple prediction flips. To align with tabular semantics, we mask at the level of serialized key:value fields (atomic in the prompt string), not individual subword tokens. Experimental validation on the Adult Income and Heart Disease benchmarks demonstrates that TabSHAP isolates critical diagnostic features, achieving significantly higher faithfulness than random baselines and XGBoost proxies. We further run a distance-metric ablation on the same test instances and TabSHAP settings: attributions are recomputed with KL or L1 replacing JSD in the similarity step (results cached per metric), and we compare deletion faithfulness across all three.

Related papers

• ControlledShifts: Towards Standardizing Robustness Evaluation in Trajectory Prediction Under Distribution Shifts [12.370065954075299]
  Trajectory prediction is central to safety in autonomous driving.<n>Many methods attempt to mitigate distribution shift degradation through data-centric or test-time adaptation approaches.<n>We introduce ControlledShifts, a framework that systematically re-splits existing trajectory datasets into in-distribution and out-of-distribution partitions.
  arXiv  Detail & Related papers  (2026-08-18T15:16:52Z)
• LimiX-2M: Mitigating Low-Rank Collapse and Attention Bottlenecks in Tabular Foundation Models [56.999481798138625]
  LimiX-2M is a 2M- parameter model that outperforms larger TabPFN-v2 and TabICL baselines on widely used benchmarks.<n>We present a unified tokenize-and-route framework for strong Tabular foundation models (TFMs)<n>Results highlight value-aware tokenization and readout-aligned routing as key levers for improving the accuracy--efficiency trade-off in TFMs.
  arXiv  Detail & Related papers  (2026-06-03T06:07:33Z)
• TabSCM: A practical Framework for Generating Realistic Tabular Data [22.41489215805826]
  We present TabSCM, a mixed-type generator that preserves causal dependencies.<n>On seven public datasets, TabSCM matches or surpasses state-of-the-art GAN, diffusion, and LLM baselines in statistical fidelity.<n>Because generation is decomposed into explicit equations, it runs up to 583$times$ faster than diffusion-only models.
  arXiv  Detail & Related papers  (2026-04-24T08:10:01Z)
• ABLE: Representing and Mapping LLMs via Attribution-Based Large-model Embedding [23.27523995613204]
  We propose ABLE (Attribution-Based Large-model Embedding), a framework that leverages the interpretability space to construct model representations.<n>By aggregating gradient-based feature attributions via a tokenizer-agnostic word-level alignment, ABLE captures model-specific input-sensitivity patterns.<n>Our training-free approach achieves competitive or superior performance in relation prediction, model routing, and benchmark score prediction.
  arXiv  Detail & Related papers  (2026-04-20T04:55:11Z)
• Routing the Lottery: Adaptive Subnetworks for Heterogeneous Data [2.5157688901171995]
  Lottery Ticket Hypothesis posits that large networks contain sparseworks, or winning tickets, that can be trained in isolation to match the performance of their dense counterparts.<n>We propose the Routing Lottery (RTL), an adaptive pruning framework that discovers multiple specialized pruningworks, called adaptive tickets, each tailored to a class, cluster semantic, or environmental condition.<n>Our results recast pruning as a mechanism for aligning model structure with data heterogeneity, paving the way toward more modular and context-aware deep learning.
  arXiv  Detail & Related papers  (2026-01-29T18:56:41Z)
• Hey, That's My Data! Label-Only Dataset Inference in Large Language Models [63.35066172530291]
  CatShift is a label-only dataset-inference framework.<n>It capitalizes on catastrophic forgetting: the tendency of an LLM to overwrite previously learned knowledge when exposed to new data.
  arXiv  Detail & Related papers  (2025-06-06T13:02:59Z)
• cDP-MIL: Robust Multiple Instance Learning via Cascaded Dirichlet Process [23.266122629592807]
  Multiple instance learning (MIL) has been extensively applied to whole slide histoparametric image (WSI) analysis. The existing aggregation strategy in MIL, which primarily relies on the first-order distance between instances, fails to accurately approximate the true feature distribution of each instance. We propose a new Bayesian nonparametric framework for multiple instance learning, which adopts a cascade of Dirichlet processes (cDP) to incorporate the instance-to-bag characteristic of the WSIs.
  arXiv  Detail & Related papers  (2024-07-16T07:28:39Z)
• AdapTable: Test-Time Adaptation for Tabular Data via Shift-Aware Uncertainty Calibrator and Label Distribution Handler [29.395855812763617]
  We propose AdapTable, a framework for adapting machine learning models to target data without accessing source data.<n>AdapTable operates in two stages: 1) calibrating model predictions using a shift-aware uncertainty calibrator, and 2) adjusting these predictions to match the target label distribution with a label distribution handler.<n>Our results demonstrate AdapTable's ability to handle various real-world distribution shifts, achieving up to a 16% improvement on the dataset.
  arXiv  Detail & Related papers  (2024-07-15T15:02:53Z)
• Quantifying Prediction Consistency Under Fine-Tuning Multiplicity in Tabular LLMs [10.494477811252034]
  Fine-tuning multiplicity can arise in Tabular LLMs on classification tasks.<n>Our work formalizes this unique challenge of fine-tuning multiplicity in Tabular LLMs.<n>We propose a novel measure to quantify consistency of individual predictions without expensive model retraining.
  arXiv  Detail & Related papers  (2024-07-04T22:22:09Z)
• Prototype-based Aleatoric Uncertainty Quantification for Cross-modal Retrieval [139.21955930418815]
  Cross-modal Retrieval methods build similarity relations between vision and language modalities by jointly learning a common representation space. However, the predictions are often unreliable due to the Aleatoric uncertainty, which is induced by low-quality data, e.g., corrupt images, fast-paced videos, and non-detailed texts. We propose a novel Prototype-based Aleatoric Uncertainty Quantification (PAU) framework to provide trustworthy predictions by quantifying the uncertainty arisen from the inherent data ambiguity.
  arXiv  Detail & Related papers  (2023-09-29T09:41:19Z)
• Leveraging Instance Features for Label Aggregation in Programmatic Weak Supervision [75.1860418333995]
  Programmatic Weak Supervision (PWS) has emerged as a widespread paradigm to synthesize training labels efficiently. The core component of PWS is the label model, which infers true labels by aggregating the outputs of multiple noisy supervision sources as labeling functions. Existing statistical label models typically rely only on the outputs of LF, ignoring the instance features when modeling the underlying generative process.
  arXiv  Detail & Related papers  (2022-10-06T07:28:53Z)
• Confident Sinkhorn Allocation for Pseudo-Labeling [40.883130133661304]
  Semi-supervised learning is a critical tool in reducing machine learning's dependence on labeled data. This paper studies theoretically the role of uncertainty to pseudo-labeling and proposes Confident Sinkhorn Allocation (CSA) CSA identifies the best pseudo-label allocation via optimal transport to only samples with high confidence scores.
  arXiv  Detail & Related papers  (2022-06-13T02:16:26Z)
• Examining and Combating Spurious Features under Distribution Shift [94.31956965507085]
  We define and analyze robust and spurious representations using the information-theoretic concept of minimal sufficient statistics. We prove that even when there is only bias of the input distribution, models can still pick up spurious features from their training data. Inspired by our analysis, we demonstrate that group DRO can fail when groups do not directly account for various spurious correlations.
  arXiv  Detail & Related papers  (2021-06-14T05:39:09Z)
• Certified Robustness to Label-Flipping Attacks via Randomized Smoothing [105.91827623768724]
  Machine learning algorithms are susceptible to data poisoning attacks. We present a unifying view of randomized smoothing over arbitrary functions. We propose a new strategy for building classifiers that are pointwise-certifiably robust to general data poisoning attacks.
  arXiv  Detail & Related papers  (2020-02-07T21:28:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.