🧑‍🔬 Cheng Deng

🏛️ Affiliations

🎓University of EdinburghEdinburgh · United Kingdom2025–2026 · 8 paperscurrent🎓Xidian UniversityXi'an · China2026 · 2 paperscurrent🎓Hohai UniversityNanjing · China2026 · 1 papercurrent

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

🧭 Fields, topics and concepts

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

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. NovelQA: Benchmarking Question Answering on Documents Exceeding 200K Tokens (2024 · 29 citations)
   Current LLMs accept context windows of 200K+ tokens, but long-context benchmarks typically average around 60K tokens.
2. Revealing and Enhancing Core Visual Regions: Harnessing Internal Attention Dynamics for Hallucination Mitigation in LVLMs (2026 · 5 citations)
   Large vision-language models (LVLMs) often hallucinate, producing content that contradicts the image or ignores the user's request.
3. Dual Activation-Weight Sparsity: A Training-Free Framework for Efficient Large Language Model Compression (2026)
   Running large language models is costly. Existing compression tools usually address only one side of the problem. Weight pruning (SparseGPT, WANDA) produces fixed masks, while activation sparsity (TEAL) skips small inputs uniformly. Combining the two has so far required extra machinery, such as the SVD components in…

All works

6 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

1. NovelQA: Benchmarking Question Answering on Documents Exceeding 200K TokensarXiv:2403.12766Cunxiang Wang, Ruoxi Ning, Boqi Pan, et al.2024 · 29 citations
2. Revealing and Enhancing Core Visual Regions: Harnessing Internal Attention Dynamics for Hallucination Mitigation in LVLMsFindings of the Association for Computational LinguisticsGuangtao Lyu, Qi Liu, Chenghao Xu, et al.2026 · 5 citations
3. Dual Activation-Weight Sparsity: A Training-Free Framework for Efficient Large Language Model CompressionAnnual Meeting of the Association for Computational Linguistics (ACL)Luoyang Sun, Guangyan Li, Cheng Deng, et al.2026
4. GR1: Reinforcement-Enhanced LLM for Geoscience Reasoning ↗Findings of the Association for Computational Linguistics: ACL 2026 · no summary yetYule Xie et al.2026
5. Fisher-Driven Adaptive Locating for Knowledge Editing in Large Language Models ↗Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetChenghao Xu et al.2026
6. Rotation Control Unlearning: Quantifying and Controlling Continuous Unlearning for LLM with The Cognitive Rotation Space ↗Findings of the Association for Computational Linguistics: ACL 2026 · no summary yetXiang Zhang et al.2026

🤝 Co-authors here

12 of 13 co-authors with a page, the most papers together first.

• Cunxiang Wang
• Fen Fang
• Guangsheng Bao
• Jian Zhao
• Jun Wang
• Muli Yang
• Qi Liu
• Qian Wang
• Qipeng Guo
• Ruoxi Ning
• Xiangkun Hu
• Yue Zhang

Venues

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

• Annual Meeting of the Association for Computational Linguistics (ACL) ×2: ACL 2026
• Findings of the Association for Computational Linguistics: ACL 2026 ×2
• Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2026

🔗 Cited by

Papers of our library cite their works 33 times (of the 149,439 papers whose bibliographies we read); 3 citing papers with a page here →.

Updated 1 November 2026

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