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Yue Liu

Yue Liu has 4 papers in our library, in 2024–2026, most of them on Language models & pretraining, Question answering; often with Anuj Kumar, Bofei Gao, Bryan Hooi.

4 papers in our library · 2024–2026

OpenAlex · ORCID · h-index 5 · 33 works · 205 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 69 times in our library · ⭐ Paper Stars ranking: #6988

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

🎓Hunan Agricultural UniversityChangsha · China2009–2012 · 4 papers🔬Institute of Geographic Sciences and Natural Resources ResearchBeijing · China2001–2014 · 3 papers🎓Geely University of ChinaChengdu · China2024 · 2 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.

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 4 worksArts and Humanities · 3 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 3 worksInformation Systems · 2 worksSoftware · 1 work

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 2 worksQuestion answering · 1 work

 Works by year

2020s

20261 paper20252 papers20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. In-context Learning with Retrieved Demonstrations for Language Models: A Survey (2024 · 54 citations)
    Large language models can solve new tasks from a few input–output examples placed in the prompt. This is called few-shot in-context learning (ICL), and its accuracy depends heavily on which examples are chosen. Early approaches used one fixed demonstration set per task, written by hand, sampled at random or picked by…
  2. Can Indirect Prompt Injection Attacks Be Detected and Removed? (2025 · 30 citations)
    Indirect prompt injection happens when an attacker plants malicious instructions in external content, such as web pages a tool retrieves, and the LLM then follows them instead of the user's request.
  3. Kimi-Dev: Agentless Training as Skill Prior for SWE-Agents (2025 · 4 citations)
    Automated GitHub issue fixing, as measured by SWE-bench, has split into two camps. Multi-turn SWE-Agents are flexible but hard to train end-to-end. Pipeline-style Agentless methods are easy to optimize with verifiable rewards but rigid. The authors argue the two approaches can be combined: training on Agentless…
  4. Knowledge Extraction on Semi-Structured Content: Does It Remain Relevant for Question Answering in the Era of LLMs? (2026 · 1 citation)
    LLMs now answer questions over semi-structured web content (tables, attribute–value lists, free-form layouts) quite well.

 Co-authors here

12 of 20 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 69 times (of the 149,439 papers whose bibliographies we read); 13 citing papers with a page here →.

Updated

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