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New research questions transformer generalization claims due to shortcut learning

By PulseAugur Editorial · [1 sources] ·

A new arXiv paper investigates compositional generalization in transformer models, finding that previous evaluations have been inconsistent due to overlooking factors like composition equivalence and module coverage. The study reveals that models often exploit shortcuts like composition equivalence, leading to an overestimation of their true generalization abilities. When these shortcuts are removed, performance drops significantly, highlighting the need for more robust benchmark designs that account for these factors. AI

IMPACT Highlights potential overestimation of transformer generalization capabilities, urging for improved benchmark design.

RANK_REASON The cluster contains an academic paper detailing research findings on transformer models. [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 →

New research questions transformer generalization claims due to shortcut learning

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The cluster contains an academic paper detailing research findings on transformer models. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Purva Pruthi, Andrew Yuan, Alexander D'Amour, David Jensen ·

    Evaluating Compositional Generalization in Transformers: The Role of Composition Equivalence and Module Coverage

    arXiv:2611.13498v1 Announce Type: cross Abstract: Compositional generalization is an important form of out-of-distribution generalization, defined as the ability to train on some combinations of modules (e.g., functions such as sort, filter) and then generalize to unseen combinat…

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