🧑‍🔬 Yi Fung

🏛️ Affiliations

🎓Hong Kong University of Science and TechnologyHong Kong · Hong Kong2025–2026 · 16 paperscurrent🎓University of Illinois Urbana-ChampaignUrbana · United States2020–2025 · 32 papers🎓University of Massachusetts AmherstAmherst Center · United States2018–2023 · 6 papers🎓Yale UniversityNew Haven · United States2025 · 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. Tool Learning with Foundation Models (2023 · 378 citations)
   This survey asks how large pretrained models can learn to operate external tools such as search engines, calculators, databases, image generators and robots.
2. CREATOR: Tool Creation for Disentangling Abstract and Concrete Reasoning of Large Language Models (2023 · 72 citations)
   Tool-augmented LLMs usually depend on a fixed set of existing APIs. They must plan and execute in a single pass of implicit reasoning, which breaks easily, and they handle errors poorly. CREATOR addresses this by having the model write its own tools instead of only calling given ones. The framework has four stages.…

All works

8 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. Tool Learning with Foundation ModelsACM Computing SurveysYujia Qin, Shengding Hu, Yankai Lin, et al.2023 · 378 citations
2. R-Tuning: Instructing Large Language Models to Say ‘I Don't Know'Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) · original ↗ · no summary yetHanning Zhang et al.2024 · 95 citations
3. CREATOR: Tool Creation for Disentangling Abstract and Concrete Reasoning of Large Language ModelsFindings of the Association for Computational LinguisticsCheng Qian, Chi Han, Yi Fung, et al.2023 · 72 citations
4. Word Embeddings Are Steers for Language ModelsProceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · original ↗ · no summary yetChi Han et al.2024 · 47 citations
5. Persona-DB: Efficient Large Language Model Personalization for Response Prediction with Collaborative Data RefinementProceedings of the 31st International Conference on Computational Linguistics · original ↗ · no summary yetChenkai Sun et al.2025 · 27 citations
6. VLM2-Bench: A Closer Look at How Well VLMs Implicitly Link Explicit Matching Visual CuesProceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · original ↗ · no summary yetJianshu Zhang et al.2025 · 12 citations
7. Let's Reason Formally: Natural-Formal Hybrid Reasoning Enhances LLM's Math CapabilityProceedings of the 2025 Conference on Empirical Methods in Natural Language Processing · original ↗ · no summary yetRuida Wang et al.2025 · 7 citations
8. Mathematical Proof as a Litmus Test: Revealing Failure Modes of Advanced Large Reasoning ModelsProceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · original ↗ · no summary yetDadi Guo et al.2026 · 5 citations

🤝 Co-authors here

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

• Chi Han ×2
• Yujia Qin ×2
• Chaojun Xiao
• Ganqu Cui
• Heng Ji
• Ning Ding
• Shengding Hu
• Weize Chen
• Yankai Lin
• Yufei Huang
• Yusheng Su
• Zhiyuan Liu

Venues

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

• Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) ×3
• ACM Computing Surveys: ACM Computing Surveys 2023
• Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2023
• Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)
• Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
• Proceedings of the 31st International Conference on Computational Linguistics

🔗 Cited by

Papers of our library cite their works 405 times (of the 141,840 papers whose bibliographies we read); 43 citing papers with a page here →.

Updated 24 October 2026

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