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Heshan Liu

2 accepted papers

2025

Conv-Basis: A New Paradigm for Efficient Attention Inference and Gradient Computation in Transformers

EMNLP 2025

The self-attention mechanism is key to the success of transformers in recent large language models (LLMs). However, the quadratic computational cost, O(n 2 ) , with respect to the input sequence length n poses a significant obstacle to further improvement and scalability in longer contexts.In this w

Cited by 0SourcePDFScholar
2025

Simulate and Eliminate: Revoke Backdoors for Generative Large Language Models

AAAI 2025technical

With rapid advances, generative large language models (LLMs) dominate various Natural Language Processing (NLP) tasks from understanding to reasoning. Yet, language models' inherent vulnerabilities may be exacerbated due to increased accessibility and unrestricted model training on massive data. A m…