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Gongshen Liu

Gongshen Liu has 2 papers in our library, in 2018–2024, most of them on Bias, fairness & safety; often with Zhuosheng Zhang, Hai Zhao, Rui Wang.

2 papers in our library · 2018–2024

OpenAlex · ORCID · h-index 22 · 217 works · 2,172 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 137 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #4428

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

🎓Shanghai Jiao Tong UniversityShanghai · China2003–2026 · 174 paperscurrent

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.

 Concepts, shelves and topics

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

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 worksSafety Research · 1 workSocial Sciences · 1 work

🧭 Topics 1Research subjects and languages the work is about.Bias, fairness & safety · 1 work

 Works by year

2020s

20241 paperR-Judge: Benchmarking Safety Risk Awareness for LLM Agents

2010s

20181 paperModeling Multi-turn Conversation with Deep Utterance Aggregation

 Papers

The most cited first (OpenAlex and our library).

  1. Modeling Multi-turn Conversation with Deep Utterance Aggregation (2018 · 152 citations)
    The paper tackles retrieval-based response selection for multi-turn dialogue. Earlier systems either concatenate the whole history, which adds noise, or match the candidate against each turn without weighing which turns and words matter. The authors propose Deep Utterance Aggregation (DUA), which has five modules:
  2. R-Judge: Benchmarking Safety Risk Awareness for LLM Agents (2024 · 97 citations)
    LLM-based agents that run tools and act in real environments can cause harm through their actions, not just their text.

All works

3 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

  1. Modeling Multi-turn Conversation with Deep Utterance AggregationInternational Conference on Computational Linguistics (COLING)Zhuosheng Zhang, Jiangtong Li, Pengfei Zhu, et al.2018 · 152 citations
  2. R-Judge: Benchmarking Safety Risk Awareness for LLM AgentsFindings of the Association for Computational LinguisticsTongxin Yuan, Zhiwei He, Lingzhong Dong, et al.2024 · 97 citations
  3. Improving Non-autoregressive Machine Translation with Error Exposure and Consistency RegularizationarXiv:2402.09725 · original ↗ · no summary yetXinran Chen, Sufeng Duan, Gongshen Liu2024 · 2 citations

 Co-authors here

4 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 137 times (of the 141,840 papers whose bibliographies we read); 6 citing papers with a page here →.

Updated

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