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Jiang Bian

Jiang Bian has 2 papers in our library, in 2019–2026, most of them on Language models & pretraining, Information extraction, Reasoning; often with Qingsong Wen, Wei Hong Xu, Yiyuan Yang.

2 papers in our library · 2019–2026

OpenAlex · ORCID · Wikidata · h-index 29 · 129 works · 4,398 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 125 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #5300

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🏢Microsoft Research AsiaBeijing · China2013–2026 · 79 papersEmploymentcurrent🏢MicrosoftRedmond · United States2019–2026 · 17 paperscurrent🏢BaiduBeijing · China2023–2024 · 2 papers🏢YahooSunnyvale · United States2011–2012 · 2 papers🎓Georgia Institute of TechnologyAtlanta · United StatesEducation · PhD

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.

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 2 worksArtificial Intelligence · 2 worksArts and Humanities · 1 workLanguage and Linguistics · 1 workComputer Networks and Communications · 1 workSignal Processing · 1 work

🧭 Topics 3Research subjects and languages the work is about.Information extraction · 1 workLanguage models & pretraining · 1 workReasoning · 1 work

 Works by year

2020s

20261 paper

2010s

20191 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event Extraction (2019 · 246 citations)
    Most event extraction (EE) systems only find event arguments within a single sentence. In financial documents, however, the arguments of one event are often spread over several sentences, and one document may describe several events. This work targets document-level EE (DEE) on Chinese financial announcements…
  2. 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…

All works

4 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. Doc2EDAG: An End-to-End Document-level Framework for Chinese Financial Event ExtractionConference on Empirical Methods in Natural Language Processing (EMNLP)Shun Zheng, Wei Cao, Wei Xu, et al.2019 · 246 citations
  2. Reinforcement Learning with Verifiable Rewards Implicitly Incentivizes Correct Reasoning in Base LLMs ↗arXiv:2506.14245 · no summary yetXumeng Wen et al.2025 · 62 citations
  3. Evaluating the robustness and readiness of large frontier models in health AI applications ↗Nature Medicine · no summary yet裕二 池谷 et al.2026 · 4 citations
  4. 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

 Co-authors here

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

Venues

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

 Cited by

Papers of our library cite their works 125 times (of the 149,439 papers whose bibliographies we read); 13 citing papers with a page here →.

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