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Ke Yang

Ke Yang has 4 papers in our library, in 2025–2026, most of them on Language models & pretraining, Dialogue systems, Information retrieval & search; often with Anthony Rios, ChengXiang Zhai, Erfan Nourbakhsh.

4 papers in our library · 2025–2026

OpenAlex · h-index 0 · 4 works · 0 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 29 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #8456

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 Illinois Urbana-ChampaignUrbana · United States2026 · 1 papercurrent

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 9The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 4 worksComputer Science · 4 worksArtificial Intelligence · 4 worksLanguage and Linguistics · 4 worksInformation Systems · 2 worksBiochemistry, Genetics and Molecular Biology · 1 workMolecular Biology · 1 workSafety Research · 1 workSocial Sciences · 1 work

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 1 workInformation retrieval & search · 1 workDialogue systems · 1 work

 Works by year

2020s

20263 papers20251 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Persona-DB: Efficient Large Language Model Personalization for Response Prediction with Collaborative Data Refinement (2025 · 29 citations)
    The paper addresses context efficiency in retrieval-augmented LLM personalization: how to keep predictions of a user's reactions accurate while placing fewer retrieved items into the prompt.
  2. When Retrieval Doesn't Help: A Large-Scale Study of Biomedical RAG (2026 · 1 citation)
    Retrieval-augmented generation (RAG) is often recommended for medical question answering, and earlier benchmarks such as MIRAGE/MedRAG reported large gains.
  3. BiasGRPO: Stabilizing Bias Mitigation in High-Variance Reward Landscapes via Group-Relative Policy Optimization (2026)
    Reducing social bias in LLMs is hard to optimise because no single "correct" unbiased answer exists, so reward signals are noisy and subjective.
  4. TRACER: Early Failure Detection for Task-Oriented Dialogue (2026)
    Most evaluation of task-oriented dialogue (TOD) systems judges a conversation only after it ends, when it is too late to rescue it.

All works

6 works of theirs in our library, the most cited first; 4 with a page here, the others link their original until we summarise them.

  1. Persona-DB: Efficient Large Language Model Personalization for Response Prediction with Collaborative Data RefinementInternational Conference on Computational Linguistics (COLING)Chenkai Sun, Ke Yang, Revanth Gangi Reddy, et al.2025 · 29 citations
  2. When Retrieval Doesn't Help: A Large-Scale Study of Biomedical RAGBioNLPErfan Nourbakhsh, Rocky Slavin, Ke Yang, et al.2026 · 1 citation
  3. BiasGRPO: Stabilizing Bias Mitigation in High-Variance Reward Landscapes via Group-Relative Policy OptimizationFindings of the Association for Computational LinguisticsSaket Reddy, Ke Yang, ChengXiang Zhai2026
  4. TRACER: Early Failure Detection for Task-Oriented DialogueAnnual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)Erfan Nourbakhsh, Rocky Slavin, Ke Yang, et al.2026
  5. Knowledge-Infused Multi-Bit Watermarking for RAG Knowledge Bases ↗Findings of the Association for Computational Linguistics: ACL 2026 · no summary yetKe Yang et al.2026
  6. You Can Have a Second Chance: Unbiased and Multi-bit Watermarking for Diffusion Language Models with Regret-based Remasking ↗Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetKe Yang et al.2026

 Co-authors here

8 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 29 times (of the 149,439 papers whose bibliographies we read); 11 citing papers with a page here →.

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