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Feng Nan

Feng Nan has 4 papers in our library, in 2021–2025, most of them on Summarization, Language models & pretraining, Question answering; often with Bing Xiang, Dejiao Zhang, Henghui Zhu.

4 papers in our library · 2021–2025

OpenAlex · ORCID · h-index 5 · 22 works · 74 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 248 times in our library · ⭐ Paper Stars ranking: #2944

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

🎓Duke UniversityDurham · United States2026 · 1 papercurrent🎓Shandong Jiaotong UniversityJinan · China2016–2020 · 4 papers🎓Huai'an UniversityHuai'an · China2025 · 2 papers🎓Chengdu University of Traditional Chinese MedicineChengdu · China2023–2024 · 2 papers🏢China Electronics Technology Group CorporationBeijing · China2023–2024 · 2 papers🏥Second Affiliated Hospital of Nanchang UniversityNanchang · China2023 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 8 more institutions on single bylines are not listed.

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📚 Shelves 5The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 4 worksComputer Science · 4 worksArtificial Intelligence · 4 worksLanguage and Linguistics · 4 worksComputer Vision and Pattern Recognition · 1 work

🧭 Topics 5Research subjects and languages the work is about.Summarization · 2 worksQuestion answering · 1 workDialogue systems · 1 workEvaluation & benchmarks · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20251 paperToolSandbox: A Stateful, Conversational, Interactive Evaluation Benchmark for LLM Tool Use Capabilities20213 papersFeng Nan's papers of 2021

 Papers

The most cited first (OpenAlex and our library).

  1. Supporting Clustering with Contrastive Learning (2021 · 239 citations)
    Deep clustering methods that rely on distances often fail when semantic categories overlap heavily in the embedding space at the start of training.
  2. Entity-level Factual Consistency of Abstractive Text Summarization (2021 · 205 citations)
    Abstractive summarizers often hallucinate named entities, inserting people, places or organizations that never occur in the source article. ROUGE does not detect this kind of error. The authors address it with three entity-level metrics computed with spaCy NER and lenient, case-insensitive n-gram matching:
  3. Improving Factual Consistency of Abstractive Summarization via Question Answering (2021 · 128 citations)
    Abstractive summarizers such as fine-tuned BART-large often produce fluent summaries that are not supported by the source document or even contradict it. Standard MLE training rewards n-gram overlap, not faithfulness. The authors tackle this problem in two steps.
  4. ToolSandbox: A Stateful, Conversational, Interactive Evaluation Benchmark for LLM Tool Use Capabilities (2025 · 67 citations)
    ToolSandbox is a benchmark from Apple for testing how well LLMs use tools in realistic, multi-turn settings.

 Co-authors here

12 of 14 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 248 times (of the 141,840 papers whose bibliographies we read); 18 citing papers with a page here →.

Updated

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