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Zhiqiang Ma

Zhiqiang Ma has 3 papers in our library, in 2022–2026, most of them on Language models & pretraining, Question answering, Information extraction; often with Pranav Shetty, Xiaomo Liu, Charese Smiley.

3 papers in our library · 2022–2026

OpenAlex · h-index 0 · 8 works · 0 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 102 times in our library · ⭐ Paper Stars ranking: #5949

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

 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 · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksLanguage and Linguistics · 3 worksComputer Vision and Pattern Recognition · 2 worksInformation Systems · 1 work

🧭 Topics 6Research subjects and languages the work is about.Dialogue systems · 1 workQuestion answering · 1 workReasoning · 1 workEvaluation & benchmarks · 1 workInformation extraction · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20262 papers20221 paper

 Papers

The most cited first (OpenAlex and our library).

  1. ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering (2022 · 172 citations)
    The paper asks whether current models can follow multi-step arithmetic that unfolds across a dialogue, rather than within one isolated question.
  2. 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…
  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…

 Co-authors here

9 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 102 times (of the 149,439 papers whose bibliographies we read); 23 citing papers with a page here →.

Updated

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