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Xuexiong Luo

3 accepted papers

2026

Detoxifying Large Language Models via Localized Feature Editing with Sparse Autoencoders

IJCAI 2026

Large Language Models (LLMs) powerful generative capabilities also pose significant risks, underscoring the need for effective detoxification methods to ensure safer deployment. Due to the polysemantic nature of LLM neurons, recent neuron intervention methods inevitably entangle unrelated concepts,

Cited by 0Scholar
2026

From Chaos to Cure: A Prefix Heuristics Guided Model-Agnostic Adaptive Detoxification Framework

AAAI 2026technical

The impressive performance of large language models (LLMs) also brings inherent toxicity risks, prompting the need for effective detoxification to support responsible deployment. Prevailing methods generally follow an inflexible model-specific fashion, addressing only individual models or model fami

Cited by 0SourcePDFScholar
2024

Graph Neural Networks for Brain Graph Learning: A Survey

IJCAI 2024poster

Exploring the complex structure of the human brain is crucial for understanding its functionality and diagnosing brain disorders. Thanks to advancements in neuroimaging technology, a novel approach has emerged that involves modeling the human brain as a graph-structured pattern, with different brain…