🧑‍🔬 Zhen Tan

🏛️ Affiliations

🎓Arizona State UniversityTempe · United States2026 · 3 paperscurrent🎓University of North Carolina at Chapel HillChapel Hill · United States2026 · 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. Large Language Models for Data Annotation and Synthesis: A Survey (2024 · 262 citations)
   Labeling or generating training data by hand is slow, expensive and often requires domain experts. This survey examines how LLMs such as GPT-4, Gemini and LLaMA-2 can automate that work. It is a literature review, so it reports no new experiments. The authors frame the setting with two roles: an annotator model A…
2. In Prospect and Retrospect: Reflective Memory Management for Long-term Personalized Dialogue Agents (2025 · 46 citations)
   LLM dialogue agents keep no state between sessions, so long-term personalization depends on external memory.
3. Contextualization Distillation from Large Language Model for Knowledge Graph Completion (2024 · 30 citations)
   Pre-trained language models (PLMs) used for knowledge graph completion (KGC) depend on textual side information, such as Wikipedia articles or synset definitions.
4. Is Chain-of-Thought Reasoning of LLMs a Mirage? A Data Distribution Lens (2026 · 21 citations)
   The paper asks why chain-of-thought (CoT) prompting sometimes yields convincing step-by-step reasoning and sometimes collapses.
5. Learning from Diverse Reasoning Paths with Routing and Collaboration (2025 · 11 citations)
   Compact student models trained by black-box distillation usually see only one teacher rationale per question, which captures little of the teacher's reasoning repertoire.
6. AnyMAC: Cascading Flexible Multi-Agent Collaboration via Next-Agent Prediction (2025 · 10 citations)
   LLM-based multi-agent systems usually link agents through fixed or learned graphs, often DAGs. These graphs allow each agent to be used only once per round and let agents see only their neighbours' messages. AnyMAC replaces the graph with a sequence. At each step, Next-Agent Prediction (NAP) picks which role to call…
7. Metacognitive Self-Correction for Multi-Agent System via Prototype-Guided Next-Execution Reconstruction (2026 · 3 citations)
   LLM-based multi-agent systems (MAS) are fragile: one agent's mistake can spread through the collaboration and derail the whole run.

🤝 Co-authors here

12 of 19 co-authors with a page, the most papers together first.

• Song Wang ×4
• Tianlong Chen ×4
• Dawei Li ×3
• Huan Liu ×3
• Jundong Li ×3
• Bohan Jiang ×2
• Zihan Chen ×2
• Hamid Palangi
• I-Hung Hsu
• Jun Yan
• Long Le
• Lu Cheng

Venues

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

• Conference on Empirical Methods in Natural Language Processing (EMNLP) ×3: EMNLP 2025 ×2, EMNLP 2024
• Findings of the Association for Computational Linguistics ×3: Findings of the Association for Computational Linguistics 2026 ×2, Findings of the Association for Computational Linguistics 2024
• Annual Meeting of the Association for Computational Linguistics (ACL): ACL 2025

🔗 Cited by

Papers of our library cite their works 177 times (of the 149,439 papers whose bibliographies we read); 46 citing papers with a page here →.

Updated 31 October 2026

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