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Xiaoxi Li

Xiaoxi Li has 4 papers in our library, in 2025, most of them on Information retrieval & search, Language models & pretraining, Reasoning; often with Jiajie Jin, Zhicheng Dou, Guanting Dong.

4 papers in our library · 2025

OpenAlex · ORCID · Wikidata · h-index 13 · 71 works · 452 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 196 times in our library · ⭐ Paper Stars ranking: #5169

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

🎓Renmin University of ChinaBeijing · China2024–2026 · 20 papersEmploymentcurrent🏢Xiaohongshu (Little Red Book)Shanghai · China2026 · 1 papercurrent🎓Beijing Normal UniversityBeijing · China2008–2020 · 7 papers🏢IFlyTekHefei · China2021–2025 · 5 papers🎓University of Electronic Science and Technology of ChinaChengdu · China2016–2020 · 3 papers🎓Huazhong University of Science and TechnologyWuhan · China2020–2023 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 14 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).Arts and Humanities · 4 worksComputer Science · 4 worksArtificial Intelligence · 4 worksInformation Systems · 4 worksLanguage and Linguistics · 4 worksComputer Vision and Pattern Recognition · 2 works

🧭 Topics 3Research subjects and languages the work is about.Reasoning · 1 workInformation retrieval & search · 2 worksLanguage models & pretraining · 1 work

 Works by year

2020s

20254 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Search-o1: Agentic Search-Enhanced Large Reasoning Models (2025 · 211 citations)
    Large reasoning models (LRMs) such as OpenAI-o1 or QwQ produce long chains of thought, and those chains often run into gaps in what the model knows.
  2. Hierarchical Document Refinement for Long-context Retrieval-augmented Generation (2025 · 25 citations)
    Retrieval-augmented generation (RAG) often feeds an LLM very long retrieved documents. These texts carry much irrelevant material, which raises inference cost and lowers answer quality. Existing compressors either target short chunks or rank tokens with crude signals such as perplexity, so they ignore how a document…
  3. RetroLLM: Empowering Large Language Models to Retrieve Fine-grained Evidence within Generation (2025 · 19 citations)
    RetroLLM targets three weaknesses of standard retrieval-augmented generation (RAG): the cost of running a separate retriever, long inputs padded with redundant retrieved chunks, and the fact that retrieval and generation cannot be optimized together.
  4. RAG-Critic: Leveraging Automated Critic-Guided Agentic Workflow for Retrieval Augmented Generation (2025 · 14 citations)
    Retrieval-augmented generation (RAG) systems fail in many subtle ways. Typical problems include noisy retrieval, missing details and factual slips. Generic LLM judges rarely diagnose these precisely, and hand-crafted error taxonomies generalize poorly and cost a lot to build. RAG-Critic tackles this in three stages.…

 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 196 times (of the 149,439 papers whose bibliographies we read); 60 citing papers with a page here →.

Updated

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