🧑‍🔬 Qingsong Wen

🏛️ Affiliations

🎓University of OxfordOxford · United Kingdom2025–2026 · 5 paperscurrent🎓City St George's, University of LondonLondon · United Kingdom2026 · 1 papercurrent🎓The Ohio State UniversityColumbus · United States2026 · 1 papercurrent🏢Alibaba GroupSan Mateo · United States2019–2024 · 21 papers🏢Alibaba GroupHangzhou · China2020–2025 · 16 papers🎓University of Electronic Science and Technology of ChinaChengdu · China2006–2009 · 12 papers🎓Georgia Institute of TechnologyAtlanta · United States2012–2016 · 8 papers🏢Alibaba DAMO AcademyHangzhou · China2022–2023 · 6 papers🏢Alibaba GroupGeorge Town · Cayman Islands2023–2024 · 4 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 11 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. Time-MQA: Time Series Multi-Task Question Answering with Context Enhancement (2025 · 21 citations)
   Most time series research targets one numerical task at a time, such as forecasting or anomaly detection. There is also little data that pairs natural language with time series, which holds back models able to discuss and reason about temporal data. The authors propose Time-MQA, a framework that treats several time…
2. Position: LLMs Can be Good Tutors in English Education (2025 · 9 citations)
   This is a position paper. It runs no experiments and introduces no dataset. The authors argue that current LLM uses in English teaching mostly reuse standard NLP task framings and ignore educational methodology. They contend that LLMs can work as tutors that support human teachers rather than replace them. They…
3. Time-RA: Towards Time Series Reasoning for Anomaly Diagnosis with LLM Feedback (2026 · 3 citations)
   Conventional time series anomaly detection (TSAD) usually just labels data as normal or anomalous. It does not say what kind of anomaly occurred or why. The authors propose TIME-RA, which recasts TSAD as a generative reasoning problem. A (multimodal) LLM must detect an anomaly, assign it to a fine-grained category…
4. HearSay Benchmark: Do Audio LLMs Leak What They Hear? (2026)
   Audio Large Language Models (ALLMs) are trained on huge amounts of speech. The authors ask whether this training lets them work out private facts about a speaker from the voice alone, with no help from what is actually said. To test this, they built HearSay, a benchmark of more than 22,000 real recordings. Most clips…
5. FEANEL: A Benchmark for Fine-Grained Error Analysis in K-12 English Writing (2025)
   Feedback tools for school-age English learners usually stop at fixing errors (GEC) or giving holistic essay scores.

All works

15 works of theirs in our library, the most cited first; 5 with a page here, the others link their original until we summarise them.

1. DCdetector: Dual Attention Contrastive Representation Learning for Time Series Anomaly Detection ↗ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · no summary yetYiyuan Yang et al.2023 · 358 citations
2. Foundation Models for Time Series Analysis: A Tutorial and Survey ↗ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · no summary yetYuxuan Liang et al.2024 · 235 citations
3. RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time Series ↗Proceedings of the AAAI Conference on Artificial Intelligence · no summary yetQingsong Wen et al.2019 · 233 citations
4. LLM Agents for Education: Advances and Applications ↗Findings of the Association for Computational Linguistics: EMNLP 2025 · no summary yetZhendong Chu et al.2025 · 110 citations
5. AutoSurvey: Large Language Models Can Automatically Write Surveys ↗arXiv:2406.10252 · no summary yetYidong Wang et al.2024 · 91 citations
6. CulturePark: Boosting Cross-cultural Understanding in Large Language Models ↗arXiv:2405.15145 · no summary yetCheng Li et al.2024 · 70 citations
7. Position: What Can Large Language Models Tell Us about Time Series Analysis ↗arXiv:2402.02713 · no summary yetMing Jin et al.2024 · 31 citations
8. Position: Multimodal Large Language Models Can Significantly Advance Scientific Reasoning ↗Findings of the Association for Computational Linguistics: ACL 2026 · no summary yetYibo Yan et al.2026 · 23 citations
9. Time-MQA: Time Series Multi-Task Question Answering with Context EnhancementAnnual Meeting of the Association for Computational Linguistics (ACL)Yaxuan Kong, Yiyuan Yang, Yoontae Hwang, et al.2025 · 21 citations
10. Position: LLMs Can be Good Tutors in English EducationConference on Empirical Methods in Natural Language Processing (EMNLP)Jingheng Ye, Shen Wang, Deqing Zou, et al.2025 · 9 citations
11. Time-RA: Towards Time Series Reasoning for Anomaly Diagnosis with LLM FeedbackFindings of the Association for Computational LinguisticsYiyuan Yang, Zichuan Liu, Lei Song, et al.2026 · 3 citations
12. HearSay Benchmark: Do Audio LLMs Leak What They Hear?Findings of the Association for Computational LinguisticsJin Wang, Kaiwen Luo, Liang Lin, et al.2026
13. ARM2: Adaptive Reasoning Model with Vision Understanding and Executable Code ↗Findings of the Association for Computational Linguistics: ACL 2026 · no summary yetJian Xie et al.2026
14. Scaling Law for Multimodal Large Language Model Supervised Fine-Tuning ↗Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetYiFan Zhang et al.2026
15. FEANEL: A Benchmark for Fine-Grained Error Analysis in K-12 English WritingarXiv:2511.22883Jingheng Ye, Shen Wang, Jiaqi Chen, et al.2025

🤝 Co-authors here

12 of 23 co-authors with a page, the most papers together first.

• Deqing Zou ×2
• Hai-Tao Zheng ×2
• Jingheng Ye ×2
• Kun Wang ×2
• Ruitong Liu ×2
• Shen Wang ×2
• Yiyuan Yang ×2
• Haoyang Li
• Irwin King
• Jiang Bian
• Jiaqi Chen
• Jin Wang

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 ×3: Findings of the Association for Computational Linguistics 2026 ×2
• Annual Meeting of the Association for Computational Linguistics (ACL) ×2: ACL 2025
• ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) ×2
• Findings of the Association for Computational Linguistics: ACL 2026 ×2
• Conference on Empirical Methods in Natural Language Processing (EMNLP): EMNLP 2025
• Findings of the Association for Computational Linguistics: EMNLP 2025
• Proceedings of the AAAI Conference on Artificial Intelligence

🔗 Cited by

Papers of our library cite their works 236 times (of the 149,439 papers whose bibliographies we read); 57 citing papers with a page here →.

Updated 1 November 2026

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/authors/qingsong-wen/