🧑‍🔬 Zhaoxuan Tan

🏛️ Affiliations

🎓University of Notre DameNotre Dame · United States2023–2026 · 35 paperscurrent🏢GoogleMountain View · United States2026 · 1 papercurrent🎓Xi'an Jiaotong UniversityXi'an · China2021–2025 · 9 papers🏢AmazonSeattle · United States2025 · 2 papers🎓University of WashingtonSeattle · United States2022–2023 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 3 more institutions on single bylines are not listed.

🧭 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. Democratizing Large Language Models via Personalized Parameter-Efficient Fine-tuning (2024 · 83 citations)
   Most current approaches to personalizing large language models work by putting user information into the prompt.
2. Towards Safer Large Language Models through Machine Unlearning (2024 · 71 citations)
   Large language models can still give dangerous answers to harmful prompts. Earlier unlearning methods based on gradient ascent suppress such answers but tend to damage replies to ordinary requests. The authors propose Selective Knowledge negation Unlearning (SKU), a two-stage framework that removes harmful knowledge…

All works

13 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

1. Can Language Models Solve Graph Problems in Natural Language?arXiv:2305.10037 · original ↗ · no summary yetHeng Wang et al.2023 · 120 citations
2. Democratizing Large Language Models via Personalized Parameter-Efficient Fine-tuningConference on Empirical Methods in Natural Language Processing (EMNLP)Zhaoxuan Tan, Qingkai Zeng, Yijun Tian, et al.2024 · 83 citations
3. Towards Safer Large Language Models through Machine UnlearningFindings of the Association for Computational LinguisticsZheyuan Liu, Guangyao Dou, Zhaoxuan Tan, et al.2024 · 71 citations
4. Personalized Pieces: Efficient Personalized Large Language Models through Collaborative EffortsProceedings of the 2024 Conference on Empirical Methods in Natural Language Processing · original ↗ · no summary yetZhaoxuan Tan, Zheyuan Liu, Meng Jiang2024 · 39 citations
5. Protecting Privacy in Multimodal Large Language Models with MLLMU-BenchProceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) · original ↗ · no summary yetZheyuan Liu et al.2025 · 22 citations
6. Enhancing Mathematical Reasoning in LLMs by Stepwise CorrectionProceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · original ↗ · no summary yetZhenyu Wu et al.2025 · 14 citations
7. Modality-Aware Neuron Pruning for Unlearning in Multimodal Large Language ModelsProceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · original ↗ · no summary yetZheyuan Liu et al.2025 · 11 citations
8. Agentic Conversational Search with Contextualized Reasoning via Reinforcement LearningFindings of the Association for Computational Linguistics: ACL 2026 · original ↗ · no summary yetFengran Mo et al.2026 · 2 citations
9. Instant Personalized Large Language Model Adaptation via HypernetworkProceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · original ↗ · no summary yetZhaoxuan Tan et al.2026 · 2 citations
10. Legislator Representation Learning with Social Context and Expert KnowledgearXiv:2108.03881 · original ↗ · no summary yetShangbin Feng et al.2021 · 2 citations
11. MTMCS-Bench: Evaluating Contextual Safety of Multimodal Large Language Models in Multi-Turn DialoguesFindings of the Association for Computational Linguistics: ACL 2026 · original ↗ · no summary yetZheyuan Liu et al.2026 · 1 citation
12. From Personal to Collective: On the Role of Local and Global Knowledge in LLM PersonalizationFindings of the Association for Computational Linguistics: ACL 2026 · original ↗ · no summary yetZehong Wang et al.2026
13. Generalizable LLM Learning of Graph Synthetic Data with Post-training AlignmentFindings of the Association for Computational Linguistics: ACL 2026 · original ↗ · no summary yetYizhuo Zhang et al.2026

🤝 Co-authors here

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

• Meng Jiang ×2
• Yijun Tian ×2
• Zheyuan Liu ×2
• Bing Yin
• Qingkai Zeng

Venues

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

• Findings of the Association for Computational Linguistics: ACL 2026 ×4
• Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) ×3
• Conference on Empirical Methods in Natural Language Processing (EMNLP) ×2: EMNLP 2024
• Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2024
• Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

🔗 Cited by

Papers of our library cite their works 247 times (of the 141,840 papers whose bibliographies we read); 9 citing papers with a page here →.

Updated 24 October 2026

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