🧑‍🔬 Xiaomo Liu

🏛️ Affiliations

🎓Peking UniversityBeijing · China2013–2026 · 18 paperscurrent🏥Peking University School and Hospital of StomatologyBeijing · China2013–2026 · 10 paperscurrent🔬J.P. MorganLondon · United Kingdom2023–2026 · 4 paperscurrent🏢Thomson ReutersNew York · United States2015–2017 · 14 papers🏢JPMorgan Chase & CoNew York · United States2023–2025 · 8 papers🏛️National Medical Products AdministrationBeijing · China2022–2025 · 6 papers

🏛️S&P Global IncNew York · United States2018Employment since 2018 · Director of Data Science, Data Science

🏛️Thomson Reuters CorpNew York · United States2013–2018Employment since 2013 · Research Scientist, Thomson Reuters ResearchEmployment 2015–2018 · Senior Research Scientist, Research and Development

🎓Virginia TechBlacksburg · United States2006–2013Education 2006–2013 · PhD, Computer Science🎓Chalmers University of TechnologyGothenburg · Sweden2003–2005Education 2003–2005 · Master, Complex Adaptive System

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

🧭 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. Detecting Non-Membership in LLM Training Data via Rank Correlations (2026)
   Most auditing work on LLM training data asks whether a dataset was used (membership inference). This paper addresses the reverse question: how to show with statistical confidence that a model was not trained on a given dataset. Such evidence matters for copyright disputes, compliance checks, and agreements that…
2. Distill and Align Decomposition for Enhanced Claim Verification (2026)
   The paper tackles a weak spot in decompose-then-verify fact-checking of long LLM outputs. The step that splits sentences into subclaims is usually tuned separately from the verifier. It can therefore produce claims that are too fragmented, too coarse, or missing context for the verifier to judge well. The authors…
3. ExStrucTiny: A Benchmark for Schema-Variable Structured Information Extraction from Document Images (2026)
   Generalist vision-language models (VLMs) score well on standard document understanding benchmarks. It is less clear whether they can pull many related fields out of varied business documents and arrange them according to a schema the user defines on the fly. Existing resources do not test this well. KEE and RE…

All works

7 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

1. DocLLM: A Layout-Aware Generative Language Model for Multimodal Document Understanding ↗Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetDongsheng Wang et al.2024 · 112 citations
2. Are ChatGPT and GPT-4 General-Purpose Solvers for Financial Text Analytics? A Study on Several Typical Tasks ↗Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track · no summary yetXianzhi Li et al.2023 · 111 citations
3. CoCoLex: Confidence-guided Copy-based Decoding for Grounded Legal Text Generation ↗Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetSantosh T.Y.S.S et al.2025 · 2 citations
4. Perturb Your Data: Paraphrase-Guided Training Data Watermarking ↗arXiv:2512.17075 · no summary yetPranav Shetty et al.2025 · 1 citation
5. Detecting Non-Membership in LLM Training Data via Rank CorrelationsConference of the European Chapter of the Association for Computational Linguistics (EACL)Pranav Shetty, Mirazul Haque, Zhiqiang Ma, et al.2026
6. Distill and Align Decomposition for Enhanced Claim VerificationFindings of the Association for Computational LinguisticsJabez Magomere, Elena Kochkina, Samuel Mensah, et al.2026
7. ExStrucTiny: A Benchmark for Schema-Variable Structured Information Extraction from Document ImagesConference of the European Chapter of the Association for Computational Linguistics (EACL)Mathieu Sibue, Andrés Muñoz Garza, Samuel Mensah, et al.2026

🤝 Co-authors here

9 co-authors with a page, the most papers together first.

• Manuela Veloso ×2
• Pranav Shetty ×2
• Samuel Mensah ×2
• Zhiqiang Ma ×2
• Arturo Oncevay
• Charese Smiley
• Elena Kochkina
• Jabez Magomere
• Simerjot Kaur

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• Conference of the European Chapter of the Association for Computational Linguistics (EACL) ×2: EACL 2026 ×2
• Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) ×2
• Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2026
• Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: Industry Track

🔗 Cited by

Papers of our library cite their works 67 times (of the 149,439 papers whose bibliographies we read); 9 citing papers with a page here →.

Updated 1 November 2026

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