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Xue Liu

Xue Liu has 5 papers in our library, in 2007–2026, most of them on Speech act theory, Language models & pretraining, Dialogue systems; often with Haolun Wu, Ye Yuan, Can Chen.

5 papers in our library · 2007–2026

OpenAlex · h-index 1 · 8 works · 2 citations OpenAlex, 2026-10-14

📄 5 works in our library · 🔗 Cited 26 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #6898

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

🎓McGill UniversityMontreal · Canada2026 · 3 paperscurrent🎓Mohamed bin Zayed University of Artificial IntelligenceAbu Dhabi · United Arab Emirates2026 · 2 paperscurrent

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 8The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 5 worksComputer Science · 5 worksArtificial Intelligence · 5 worksLanguage and Linguistics · 5 worksInformation Systems · 2 worksComputer Vision and Pattern Recognition · 1 workEducation · 1 workSocial Sciences · 1 work

🧭 Topics 5Research subjects and languages the work is about.Information retrieval & search · 1 workInterpretability · 1 workLanguage models & pretraining · 1 workDialogue systems · 1 workSpeech act theory · 1 work

 Works by year

2020s

20263 papers20241 paper

2000s

20071 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Retrieval-Augmented Generation for Natural Language Processing: A Survey (2024 · 86 citations)
    Large language models store knowledge implicitly in their weights. As a result, they can produce fluent but false answers, they need costly retraining to refresh facts, and they often lack depth in specialised domains. Retrieval-augmented generation (RAG) addresses these issues by consulting an external knowledge…
  2. GAM: Hierarchical Graph-based Agentic Memory for LLM Agents (2026 · 5 citations)
    LLM agents in long conversations must absorb new information without letting passing chatter corrupt what they already know.
  3. RubricBench: Aligning Model-Generated Rubrics with Human Standards (2026 · 3 citations)
    Reward models increasingly judge LLM outputs against rubrics, which are checklists of explicit criteria, to reduce shallow biases such as favoring longer or more polished answers.
  4. Preference Heads in Large Language Models: A Mechanistic Framework for Interpretable Personalization (2026 · 1 citation)
    LLMs can adapt to a user's tone and topical interests, but current personalization methods (prompting, retrieval of user profiles, fine-tuning) treat this ability as a black box.
  5. Dynamic n-best Selection and Its Application in Dialog Act Detection (2007)
    Dialog system modules usually hand the next stage an n-best list of fixed length, even though the right number of candidates changes from input to input.

All works

6 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. Retrieval-Augmented Generation for Natural Language Processing: A SurveyArtificial Intelligence ReviewShangyu Wu, Ying Xiong, Yufei Cui, et al.2024 · 86 citations
  2. GAM: Hierarchical Graph-based Agentic Memory for LLM AgentsAnnual Meeting of the Association for Computational Linguistics (ACL)Zhaofen Wu, Hanrong Zhang, Fulin Lin, et al.2026 · 5 citations
  3. RubricBench: Aligning Model-Generated Rubrics with Human StandardsAnnual Meeting of the Association for Computational Linguistics (ACL)Junyi Zhou, Qiyuan Zhang, Yufei Wang, et al.2026 · 3 citations
  4. Preference Heads in Large Language Models: A Mechanistic Framework for Interpretable PersonalizationAnnual Meeting of the Association for Computational Linguistics (ACL)Weixu Zhang, Ye Yuan, Changjiang Han, et al.2026 · 1 citation
  5. Beyond Length Scaling: Synergizing Breadth and Depth for Generative Reward Models ↗Findings of the Association for Computational Linguistics: ACL 2026 · no summary yetQiyuan Zhang et al.2026
  6. Dynamic n-best Selection and Its Application in Dialog Act DetectionAnnual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)Junling Hu, Fabrizio Morbini, Fuliang Weng, et al.2007

 Co-authors here

12 of 22 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 26 times (of the 149,439 papers whose bibliographies we read); 8 citing papers with a page here →.

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