Elamur Research⌕
☰
home / authors / Lidan Shou

Lidan Shou

Lidan Shou has 4 papers in our library, in 2024–2026, most of them on Language models & pretraining, Vision & language, Efficient models & compression; often with Huan Li, Jun Zhang, Gang Chen.

4 papers in our library · 2024–2026

OpenAlex · ORCID · h-index 24 · 210 works · 2,132 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 161 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #6047

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓Zhejiang UniversityHangzhou · China2006–2026 · 170 papersEmploymentcurrent🎓Beijing University of Posts and TelecommunicationsBeijing · China2025–2026 · 3 paperscurrent🎓National University of SingaporeSingapore · Singapore2001–2008 · 9 papers🎓Zhejiang University of Science and TechnologyHangzhou · China2008–2020 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 4 worksComputer Science · 4 worksArtificial Intelligence · 4 worksComputer Vision and Pattern Recognition · 4 worksLanguage and Linguistics · 4 works

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 4 worksVision & language · 2 worksEfficient models & compression · 1 work

 Works by year

2020s

20262 papers20251 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Draft & Verify: Lossless Large Language Model Acceleration via Self-Speculative Decoding (2024 · 148 citations)
    Autoregressive generation in large language models is slow. Each new token needs a full, memory-bandwidth-bound pass through the network. Speculative decoding can help, but it usually depends on a separate draft model, which costs extra GPU memory and is hard to obtain for fine-tuned models such as LLaMA-2-Chat or…
  2. SpecVLM: Enhancing Speculative Decoding of Video LLMs via Verifier-Guided Token Pruning (2025 · 10 citations)
    Video LLMs turn sampled frames into very long sequences of visual tokens. This makes decoding slow and memory-bound. Pruning tokens at prefill saves compute but discards visual detail, which can lower answer quality. SpecVLM keeps output lossless by applying speculative decoding instead. A small Vid-LLM drafts…
  3. HybridKV: Hybrid KV Cache Compression for Efficient Multimodal Large Language Model Inference (2026 · 4 citations)
    Multimodal LLMs turn every image or video frame into hundreds or thousands of tokens. As a result, the key–value (KV) cache held in GPU memory during decoding grows quickly and slows inference. Earlier compression methods only decide how much cache each token, layer or head keeps, and they apply a single strategy…
  4. MM-ShiftKV: Decode-Aware Prefill-Stage KV Selection for Multimodal Large Language Models (2026)
    Multimodal LLMs turn high-resolution images into long token sequences, so the KV cache becomes a major memory and latency bottleneck.

All works

6 works of theirs in our library, the most cited first; 4 with a page here, the others link their original until we summarise them.

  1. Pyramid: A Layered Model for Nested Named Entity Recognition ↗Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · no summary yetJue Wang et al.2020 · 217 citations
  2. Draft & Verify: Lossless Large Language Model Acceleration via Self-Speculative DecodingAnnual Meeting of the Association for Computational Linguistics (ACL)Jun Zhang, Jue Wang, Huan Li, et al.2024 · 148 citations
  3. SpecVLM: Enhancing Speculative Decoding of Video LLMs via Verifier-Guided Token PruningConference on Empirical Methods in Natural Language Processing (EMNLP)Yicheng Ji, Jun Zhang, Heming Xia, et al.2025 · 10 citations
  4. HybridKV: Hybrid KV Cache Compression for Efficient Multimodal Large Language Model InferenceAnnual Meeting of the Association for Computational Linguistics (ACL)Bowen Zeng, Feiyang Ren, Jun Zhang, et al.2026 · 4 citations
  5. MM-ShiftKV: Decode-Aware Prefill-Stage KV Selection for Multimodal Large Language ModelsFindings of the Association for Computational LinguisticsJinsong Shu, Chenyang Wu, Zhongle Xie, et al.2026
  6. TokenTiming: A Dynamic Alignment Method for Universal Speculative Decoding Model Pairs ↗Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetSibo Xiao et al.2026

 Co-authors here

6 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 161 times (of the 149,439 papers whose bibliographies we read); 27 citing papers with a page here →.

Updated

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/authors/lidan-shou/