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Jing Tang

Jing Tang has 4 papers in our library, in 2018–2026, most of them on Information retrieval & search, Language models & pretraining, Summarization; often with Yujun Cai, Chengming Li, Chuanyang Zheng.

4 papers in our library · 2018–2026

OpenAlex · ORCID · h-index 24 · 143 works · 2,262 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 160 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #5075

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

🎓The Hong Kong University of Science and Technology (Guangzhou)Guangzhou · China2022–2026 · 75 paperscurrent🎓Hong Kong University of Science and TechnologyHong Kong · China2021–2026 · 53 papersEmployment since 2021 · Assistant Professor, Data Science and Analytics Thrustcurrent🎓National University of SingaporeSingapore · Singapore2018–2021 · 15 papers🎓Nanyang Technological UniversitySingapore · Singapore2014–2021 · 11 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 6 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).Computer Science · 4 worksArts and Humanities · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksInformation Systems · 1 workComputer Vision and Pattern Recognition · 2 works

🧭 Topics 5Research subjects and languages the work is about.Summarization · 1 workInformation retrieval & search · 2 worksEfficient models & compression · 1 workLanguage models & pretraining · 1 workReasoning · 1 work

 Works by year

2020s

20262 papers20231 paper

2010s

20181 paper

 Papers

The most cited first (OpenAlex and our library).

  1. A Unified Model for Extractive and Abstractive Summarization using Inconsistency Loss (2018 · 338 citations)
    The paper addresses a trade-off in neural summarization. Extractive systems pick whole sentences and score well on ROUGE, but their output reads poorly. Abstractive systems produce fluent text, yet they can drop or distort facts. The authors join both in one end-to-end network. A sentence extractor following…
  2. DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for In-Context Learning (2023 · 27 citations)
    The order and choice of demonstrations in a prompt strongly affect how well large language models solve multi-step reasoning problems.
  3. Beyond Query Bias: Candidate-Aware Iterative Refinement for Zero-Shot Composed Image Retrieval (2026)
    The paper addresses a weakness in Zero-Shot Composed Image Retrieval (ZS-CIR), where a system must find a target image from a reference image plus a modification text without task-specific training.
  4. Mitigating Coordinate Prediction Bias from Positional Encoding Failures (2026)
    Multimodal LLMs that output click coordinates as digit strings often drift on high-resolution screenshots, because visual positional encodings (VPEs) lose grounding strength in long visual contexts.

All works

6 works of theirs in our library, the most cited first; 4 with a page here, the others link their original until we summarise them.

  1. A Unified Model for Extractive and Abstractive Summarization using Inconsistency LossAnnual Meeting of the Association for Computational Linguistics (ACL)Wan-Ting Hsu, Chieh-Kai Lin, Ming-Ying Lee, et al.2018 · 338 citations
  2. Process-Driven Autoformalization in Lean 4 ↗arXiv:2406.01940 · no summary yetJianqiao Lu et al.2024 · 33 citations
  3. DQ-LoRe: Dual Queries with Low Rank Approximation Re-ranking for In-Context LearningarXiv:2310.02954Jing Xiong, Zixuan Li, Chuanyang Zheng, et al.2023 · 27 citations
  4. Proving Theorems Recursively ↗arXiv:2405.14414 · no summary yetHaiming Wang et al.2024 · 19 citations
  5. Beyond Query Bias: Candidate-Aware Iterative Refinement for Zero-Shot Composed Image RetrievalFindings of the Association for Computational LinguisticsNan Sun, Jing Tang, Lei Sun, et al.2026
  6. Mitigating Coordinate Prediction Bias from Positional Encoding FailuresFindings of the Association for Computational LinguisticsXingjian Tao, Yiwei Wang, Yujun Cai, et al.2026

 Co-authors here

12 of 18 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 160 times (of the 149,439 papers whose bibliographies we read); 21 citing papers with a page here →.

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