🧑‍🔬 Jimin Huang

🏛️ Affiliations

🏛️Chinese Academy of SciencesBeijing · China2023–2026 · 12 paperscurrent🔬Shanghai Institute of CeramicsShanghai · China2023–2026 · 11 paperscurrent🎓Yale UniversityNew Haven · United States2024–2026 · 11 paperscurrent🔬State Key Laboratory of High Performance Ceramics and Superfine MicrostructureShanghai2023–2026 · 10 paperscurrent🎓University of ManchesterManchester · United Kingdom2024–2026 · 7 papersEducationcurrent🤝FishBase Information and Research GroupLos Baños · Philippines2025–2026 · 2 paperscurrent🎓Shanghai University of Engineering ScienceShanghai · China2024–2026 · 2 paperscurrent🎓Xuzhou Medical CollegeTongshan · China2026 · 1 papercurrent🎓Wuhan UniversityWuhan · China2011–2024 · 14 papersEducation 2011–2015 · Bachelor of Engineering, Computer SchoolEducation 2015–2018 · Master of Engineering, Computer School🎓University of Chinese Academy of SciencesBeijing · China2023–2025 · 9 papers

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

🧭 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. LAiW: A Chinese Legal Large Language Models Benchmark (2025 · 35 citations)
   Chinese legal LLMs are usually judged task by task, for example on question answering or consultation. Such scores ignore whether a model actually follows the legal syllogism, the reasoning chain lawyers expect: legal norm (major premise), case facts (minor premise) and judgment (conclusion). This gap makes legal…
2. Me LLaMA: Foundation Large Language Models for Medical Applications (2024 · 33 citations)
   General-domain open LLMs such as LLaMA often lack specialized medical knowledge. Commercial systems like ChatGPT and GPT-4 are closed and hard to customize. Earlier open medical models seldom combined continual pre-training with instruction tuning, rarely drew on clinical notes and were evaluated mostly on QA. The…
3. INVESTORBENCH: A Benchmark for Financial Decision-Making Tasks with LLM-based Agent (2025 · 29 citations)
   LLM-based agents for finance are usually built for one narrow job, such as trading a single stock or only crypto, and are often tested on proprietary data.
4. Plutus: Benchmarking Large Language Models in Low-Resource Greek Finance (2025 · 12 citations)
   Greek financial text has been largely neglected by LLM research. Greek has rich morphology and few domain resources, and no benchmark has targeted Greek finance specifically. The authors fill this gap with Plutus-ben, an evaluation suite of six tasks: recognizing named entities in both numeric and textual forms…
5. FinChain: A Symbolic Benchmark for Verifiable Chain-of-Thought Financial Reasoning (2026 · 10 citations)
   Current financial QA benchmarks such as FinQA and ConvFinQA mostly score the final number a model produces.
6. Selective Preference Optimization via Token-Level Reward Function Estimation (2025 · 8 citations)
   Token-level preference alignment for LLMs usually trains on every token, which adds noise and wastes compute.
7. SAHM: A Benchmark for Arabic Financial and Shari'ah-Compliant Reasoning (2026 · 4 citations)
   Arabic financial NLP has almost no evaluation resources, even though Gulf economies and the Islamic finance industry produce large amounts of Arabic regulatory, juristic and corporate text.
8. MFMDQwen: Multilingual Financial Misinformation Detection Based on Large Language Model (2026)
   False or manipulated financial content is hard to detect because it often hides in small numerical edits, exaggerated sentiment or inverted cause–effect claims.
9. The Illusion of Specialization: Unveiling the Domain-Invariant “Standing Committee” in Mixture-of-Experts Models (2026)
   Mixture-of-Experts (MoE) language models are usually thought to gain efficiency by sending different domains to different specialist experts.

All works

10 works of theirs in our library, the most cited first; 9 with a page here, the others link their original until we summarise them.

1. LAiW: A Chinese Legal Large Language Models BenchmarkInternational Conference on Computational Linguistics (COLING)Yongfu Dai, Duanyu Feng, Jimin Huang, et al.2025 · 35 citations
2. Me LLaMA: Foundation Large Language Models for Medical ApplicationsarXiv:2402.12749Qianqian Xie, Qingyu Chen, Aokun Chen, et al.2024 · 33 citations
3. INVESTORBENCH: A Benchmark for Financial Decision-Making Tasks with LLM-based AgentAnnual Meeting of the Association for Computational Linguistics (ACL)Haohang Li, Yupeng Cao, Yangyang Yu, et al.2025 · 29 citations
4. Plutus: Benchmarking Large Language Models in Low-Resource Greek FinanceConference on Empirical Methods in Natural Language Processing (EMNLP)Xueqing Peng, Triantafillos Papadopoulos, Efstathia Soufleri, et al.2025 · 12 citations
5. FinChain: A Symbolic Benchmark for Verifiable Chain-of-Thought Financial ReasoningAnnual Meeting of the Association for Computational Linguistics (ACL)Zhuohan Xie, Daniil Orel, Rushil Thareja, et al.2026 · 10 citations
6. Selective Preference Optimization via Token-Level Reward Function EstimationConference on Empirical Methods in Natural Language Processing (EMNLP)Kailai Yang, Zhiwei Liu, Qianqian Xie, et al.2025 · 8 citations
7. UCL-Bench: A Chinese User-Centric Legal Benchmark for Large Language Models ↗Findings of the Association for Computational Linguistics: NAACL 2025 · no summary yetRuoli Gan et al.2025 · 5 citations
8. SAHM: A Benchmark for Arabic Financial and Shari'ah-Compliant ReasoningAnnual Meeting of the Association for Computational Linguistics (ACL)Rania Elbadry, Sarfraz Ahmad, Ahmed Heakl, et al.2026 · 4 citations
9. MFMDQwen: Multilingual Financial Misinformation Detection Based on Large Language ModelWorkshop on Multilinguality in the Era of Large Language ModelsZhiwei Liu, Yuyan Wang, Yuechen Jiang, et al.2026
10. The Illusion of Specialization: Unveiling the Domain-Invariant “Standing Committee” in Mixture-of-Experts ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Yan Wang, Yitao Xu, Nanhan Shen, et al.2026

🤝 Co-authors here

12 of 40 co-authors with a page, the most papers together first.

• Lingfei Qian ×4
• Qianqian Xie ×4
• Sophia Ananiadou ×4
• Xueqing Peng ×4
• Jinyan Su ×2
• Yan Wang ×2
• Yuechen Jiang ×2
• Yupeng Cao ×2
• Zhiwei Liu ×2
• Zhiyang Deng ×2
• Zining Zhu ×2
• Ahmed Heakl

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) ×6: ACL 2026 ×3, ACL 2025
• Conference on Empirical Methods in Natural Language Processing (EMNLP) ×2: EMNLP 2025 ×2
• International Conference on Computational Linguistics (COLING): COLING 2025
• Findings of the Association for Computational Linguistics
• Workshop on Multilinguality in the Era of Large Language Models: Workshop on Multilinguality in the Era of Large Language Models 2026
• Findings of the Association for Computational Linguistics: NAACL 2025

🔗 Cited by

Papers of our library cite their works 107 times (of the 149,439 papers whose bibliographies we read); 19 citing papers with a page here →.

Updated 1 November 2026

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