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Longqi Yang

Longqi Yang has 2 papers in our library, in 2026, most of them on Language models & pretraining; often with Jieyu Zhao, Pei Zhou, Ashish Sharma.

2 papers in our library · 2026

OpenAlex · ORCID · Wikidata · h-index 21 · 71 works · 2,650 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 9 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #9945

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

🎓University of Southern CaliforniaLos Angeles · United States2026 · 1 papercurrent🏢MicrosoftRedmond · United States2020–2025 · 26 papers🎓Cornell UniversityIthaca · United States2015–2019 · 16 papers🎓Cornell TechNew York · United States2015–2019 · 9 papers🎓Purdue University West LafayetteWest Lafayette · United States2024 · 2 papers🎓National University of Defense TechnologyChangsha · China2021–2022 · 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.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 8The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksComputer Science · 2 worksArts and Humanities · 1 workLanguage and Linguistics · 1 workEconomics and Econometrics · 1 workEconomics, Econometrics and Finance · 1 workSocial Sciences · 1 workSociology and Political Science · 1 work

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 1 work

 Works by year

2020s

20262 papers

 Papers

The most cited first (OpenAlex and our library).

  1. WildFeedback: Aligning LLMs With In-situ User Interactions And Feedback (2026 · 9 citations)
    Aligning LLMs with human preferences usually depends on curated preference data. Human annotation is expensive and subjective, and GPT-4-labelled synthetic data can reinforce a model's own biases. WildFeedback instead builds preference data from feedback that real users give naturally while chatting with a model. The…
  2. ProMediate: A Simulation Testbed for Evaluating Proactive Mediation in Multi-Party Negotiation (2026 · 1 citation)
    Most LLM assistants serve one user at a time, yet many real tasks depend on groups reaching agreement. There has been no systematic way to test whether an AI can step into a group discussion on its own initiative and help it move forward. ProMediate addresses this gap with a simulation testbed for proactive mediation…

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. WildFeedback: Aligning LLMs With In-situ User Interactions And FeedbackAnnual Meeting of the Association for Computational Linguistics (ACL)Taiwei Shi, Zhuoer Wang, Longqi Yang, et al.2026 · 9 citations
  2. ProMediate: A Simulation Testbed for Evaluating Proactive Mediation in Multi-Party NegotiationFindings of the Association for Computational LinguisticsZiyi Liu, Bahareh Sarrafzadeh, Pei Zhou, et al.2026 · 1 citation
  3. Reinforcing Human Behavior Simulation via Verbal Feedback ↗arXiv (Cornell University) · no summary yetWeiwei Sun et al.2026
  4. DP-RFT: Learning to Generate Synthetic Text via Differentially Private Reinforcement Fine-Tuning ↗arXiv (Cornell University) · no summary yetFangyuan Xu et al.2026

 Co-authors here

7 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 9 times (of the 149,439 papers whose bibliographies we read); 1 citing paper with a page here →.

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