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Research paper on NLP model interpretability withdrawn from arXiv

By PulseAugur Editorial · [1 sources] ·

A research paper titled "CLIF: Concept-Level Influence Functions for Transparent Bottleneck Models" was submitted to arXiv, proposing a novel method to enhance the interpretability of NLP models. The approach uses influence functions to identify impactful training samples and key concepts within Concept Bottleneck Models, allowing for data debugging and observable alterations in model behavior. However, the paper has since been withdrawn by its author. AI

IMPACT This research on model interpretability, though withdrawn, highlights the ongoing challenges and methods for understanding complex AI systems.

RANK_REASON The cluster contains a withdrawn academic paper discussing a novel research method. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Research paper on NLP model interpretability withdrawn from arXiv

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The cluster contains a withdrawn academic paper discussing a novel research method. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Yike Sun, Mingkun Xu, Mu You, Zhongzhi He, Henghua Shen, Zehan Tan, Derek F. Wong, Tao Fang ·

    CLIF: Concept-Level Influence Functions for Transparent Bottleneck Models

    arXiv:2605.19848v2 Announce Type: replace Abstract: In recent years, the black-box nature of deep learning models has limited their application in high-stakes domains such as medical diagnosis and finance, where interpretability is essential. To address this, we propose a novel a…

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