Generated draft
Subject
Inquiry on reasoning efficiency in large language models (Li Ming, Peking University)
Opening
Dear Prof. Tanaka,
Please excuse this unsolicited message. My name is Li Ming, a fourth-year student at Peking University's Department of Computer Science. I read your 2024 IEEE Transactions paper on 'Spectral Attention for Transformer Inference Efficiency' and was deeply impressed by your approach to reducing computational complexity from O(n²) to O(n log n).
Research connection
My graduation thesis focuses on knowledge distillation for LLMs in low-resource environments. I believe your spectral attention mechanism directly addresses computation cost reduction challenges I face. Your KAKENHI project on 'Foundation Technologies for Next-Generation Edge AI' (2023–2026) and its focus on edge inference optimization strongly aligns with my interests.
Closing
If you are open to it, I would be grateful to discuss the possibility of pursuing a master's degree in your laboratory. I am able to communicate in either English or Japanese. Thank you very much for your time.
Personalization map (why this content is uniquely yours)
Mapping of user material and professor evidence consumed by each paragraph
- OpeningBackground: Peking University, CS dept.Paper: 2024 IEEE Transactions — Builds credibility and prevents fabrication
- Research connectionResearch: LLM knowledge distillationKAKENHI 2023-2026 — Direct cost-reduction connection to spectral attention
- ClosingGoal: master's admission — Clear purpose statement helps professor decide
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