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Computer Science > Computation and Language

arXiv:2601.03089v1 (cs)
[Submitted on 6 Jan 2026 (this version), latest version 26 May 2026 (v2)]

Title:Grad-ELLM: Gradient-based Explanations for Decoder-only LLMs

Authors:Xin Huang, Antoni B. Chan
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Abstract:Large Language Models (LLMs) have demonstrated remarkable capabilities across diverse tasks, yet their black-box nature raises concerns about transparency and faithfulness. Input attribution methods aim to highlight each input token's contributions to the model's output, but existing approaches are typically model-agnostic, and do not focus on transformer-specific architectures, leading to limited faithfulness. To address this, we propose Grad-ELLM, a gradient-based attribution method for decoder-only transformer-based LLMs. By aggregating channel importance from gradients of the output logit with respect to attention layers and spatial importance from attention maps, Grad-ELLM generates heatmaps at each generation step without requiring architectural modifications. Additionally, we introduce two faithfulneses metrics $\pi$-Soft-NC and $\pi$-Soft-NS, which are modifications of Soft-NC/NS that provide fairer comparisons by controlling the amount of information kept when perturbing the text. We evaluate Grad-ELLM on sentiment classification, question answering, and open-generation tasks using different models. Experiment results show that Grad-ELLM consistently achieves superior faithfulness than other attribution methods.
Subjects: Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
Cite as: arXiv:2601.03089 [cs.CL]
  (or arXiv:2601.03089v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2601.03089
arXiv-issued DOI via DataCite

Submission history

From: Xin Huang [view email]
[v1] Tue, 6 Jan 2026 15:22:39 UTC (2,127 KB)
[v2] Tue, 26 May 2026 16:19:50 UTC (1,031 KB)
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