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Siliang Xu

Siliang Xu has 2 papers in our library, in 2024–2025, most of them on Language models & pretraining, Reasoning, Information retrieval & search; often with Cheng Niu, Haiyang Yu, Han Wang.

2 papers in our library · 2024–2025

OpenAlex · h-index 3 · 7 works · 113 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 116 times in our library · ⭐ Paper Stars ranking: #6278

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

🎓Fudan UniversityShanghai · China2025 · 1 papercurrent🎓University of Illinois Urbana-ChampaignUrbana · United States2023–2024 · 2 papers

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).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksInformation Systems · 1 workComputer Vision and Pattern Recognition · 1 work

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 2 worksInformation retrieval & search · 1 workReasoning · 1 work

💡 Concepts 1Key ideas and methods explained on our concept pages.Retrieval-augmented generation · 1 work

 Works by year

2020s

20251 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models (2024 · 207 citations)
    Retrieval-augmented generation (RAG) is meant to keep large language models anchored to supplied sources, yet models still produce claims that the retrieved material does not support or that contradict it.
  2. Prolonged Reasoning Is Not All You Need: Certainty-Based Adaptive Routing for Efficient LLM/MLLM Reasoning (2025 · 11 citations)
    Reasoning-tuned LLMs and MLLMs tend to produce long chain-of-thought output for every query. This costs many tokens and can even hurt accuracy on easy, fact-based questions. A pilot with Qwen2.5-0.5B on text-rich VQA and KIE data showed that direct answers were both more accurate and far cheaper than reasoning-based…

 Co-authors here

11 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 116 times (of the 149,439 papers whose bibliographies we read); 37 citing papers with a page here →.

Updated

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