Papers by Sebastian Zhao
Squeezed Attention: Accelerating Long Context Length LLM Inference (2025.acl-long)
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Coleman Richard Charles Hooper, Sehoon Kim, Hiva Mohammadzadeh, Monishwaran Maheswaran, Sebastian Zhao, June Paik, Michael W. Mahoney, Kurt Keutzer, Amir Gholami
| Challenge: | Emerging Large Language Models require long input context to perform complex tasks. |
| Approach: | They propose an algorithm to reduce the complexity of attention with respect to the fixed context length. |
| Outcome: | The proposed method reduces the complexity of attention from linear to logarithmic with respect to the fixed context length. |
An Efficient Memory-Augmented Transformer for Knowledge-Intensive NLP Tasks (2022.emnlp-main)
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| Challenge: | Existing methods rely on parametric models that store knowledge in parameters or retrieval-augmented models that have access to external knowledge sources. |
| Approach: | They propose a parametric parametric model that stores knowledge in its parameters or a retrieval-augmented model that has access to external knowledge sources. |
| Outcome: | The proposed method runs substantially faster across the board and produces more accurate results on WoW and ELI5. |