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News/Toward Localizing and Repairing Bias in Transformer Attention Heads
arxiv
PublishedJuly 15, 2026 at 4:00 AM
—neutral

Toward Localizing and Repairing Bias in Transformer Attention Heads

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Publisher summary· verbatim

arXiv:2607.12863v1 Announce Type: cross Abstract: Transformer language models are increasingly used as software components, yet biased outputs remain difficult to localize and repair inside the model. Existing fairness testing and repair methods largely operate at the input-output or retraining leve

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arxiv
Read original ↗All from arxiv →
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#fairness#bias#debugging#language-models

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arxivMAGNET: Manifold-Aware Graph Diffusion Network for Connectome Generation1harxivCounterfactual Model Testing Under Distribution Shift1harxivCentralized Critics for N-Agent Ad Hoc Teamwork1harxivWhere and What to Reward? Understanding Generalization Between Verifiable and Open-Ended Domains in Reinforcement Learning1h
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