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Jiali Zeng

Jiali Zeng has 3 papers in our library, in 2025–2026, most of them on Machine translation, Language models & pretraining, Reasoning; often with Fandong Meng, Jie Zhou, Chenglong Wang.

3 papers in our library · 2025–2026

OpenAlex · h-index 12 · 44 works · 506 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 94 times in our library · 3 more in our library without a page yet · ⭐ Paper Stars ranking: #8990

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

🎓Sun Yat-sen UniversityGuangzhou · China2023–2026 · 2 paperscurrent🔬Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)Zhuhai · China2026 · 1 papercurrent🔬State Key Laboratory of Environmental Adaptability for Industrial ProductsGuangzhou2026 · 1 papercurrent🏢TencentShenzhen · China2021–2025 · 29 papers🎓Xiamen UniversityXiamen · China2018–2023 · 10 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 · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 3 worksComputer Vision and Pattern Recognition · 2 works

🧭 Topics 4Research subjects and languages the work is about.Efficient models & compression · 1 workReasoning · 1 workLanguage models & pretraining · 1 workMachine translation · 1 work

 Works by year

2020s

20262 papers20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. ConCISE: Confidence-guided Compression in Step-by-step Efficient Reasoning (2025 · 19 citations)
    Large Reasoning Models (LRMs) such as DeepSeek-R1 tend to produce very long chains of thought, which raises inference cost.
  2. Cross-layer Attention Sharing for Pre-trained Large Language Models (2026 · 2 citations)
    Earlier work on making LLM attention more efficient mostly targets redundancy inside a layer, for example by compressing the KV cache or grouping heads.
  3. FeedTrans: Enhance Machine Translation via Feedback (2026)
    Reinforcement learning (RL) for machine translation usually rewards a model only for what it can already produce.

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. Multi-Domain Neural Machine Translation with Word-Level Domain Context Discrimination ↗Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · no summary yetJiali Zeng et al.2018 · 103 citations
  2. DeepRAG: Thinking to Retrieve Step by Step for Large Language Models ↗arXiv:2502.01142 · no summary yetXinyan Guan et al.2025 · 40 citations
  3. ConCISE: Confidence-guided Compression in Step-by-step Efficient ReasoningConference on Empirical Methods in Natural Language Processing (EMNLP)Ziqing Qiao, Yongheng Deng, Jiali Zeng, et al.2025 · 19 citations
  4. Cross-layer Attention Sharing for Pre-trained Large Language ModelsTransactions of the Association for Computational Linguistics (TACL)Yongyu Mu, Yuzhang Wu, Yuchun Fan, et al.2026 · 2 citations
  5. FeedTrans: Enhance Machine Translation via FeedbackTransactions of the Association for Computational Linguistics (TACL)Zengkui Sun, Jiali Zeng, Jiaan Wang, et al.2026
  6. Joint Optimization of Training Data and Policy in RLHF ↗Findings of the Association for Computational Linguistics: ACL 2026 · no summary yetZhuohao Yu et al.2026

 Co-authors here

12 of 20 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 94 times (of the 149,439 papers whose bibliographies we read); 17 citing papers with a page here →.

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