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Jinpeng Hu

Jinpeng Hu has 4 papers in our library, in 2025–2026, most of them on Language models & pretraining, Reasoning, Bias, fairness & safety; often with Zhuo Li, Anningzhe Gao, Xiang Wan.

4 papers in our library · 2025–2026

OpenAlex · ORCID · h-index 6 · 16 works · 245 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 18 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #8786

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

🎓Hefei University of TechnologyHefei · China2024–2026 · 12 papersEmploymentcurrent🎓Chinese University of Hong Kong, ShenzhenShenzhen · China2019–2023 · 4 papersEducation 2019–2023 · PhD🎓Shenzhen Research Institute of Big DataShenzhen · China2022–2023 · 4 papers🎓Harbin Institute of TechnologyHarbin · China2013–2019Education 2013–2017 · BachelorEducation 2017–2019 · Master

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Fields, topics and concepts

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

📚 Shelves 8The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 4 worksArts and Humanities · 4 worksComputer Science · 4 worksLanguage and Linguistics · 4 worksHealth Informatics · 2 worksMedicine · 2 worksSafety Research · 1 workSocial Sciences · 1 work

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 4 worksReasoning · 1 workBias, fairness & safety · 1 work

 Works by year

2020s

20262 papers20252 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Self-Instructed Derived Prompt Generation Meets In-Context Learning: Unlocking New Potential of Black-Box LLMs (2025 · 9 citations)
    Rewriting prompts is a common way to get better answers from LLMs that cannot be fine-tuned, such as GPT4.
  2. Psyche-R1: Towards Reliable Psychological LLMs through Unified Empathy, Expertise, and Reasoning (2026 · 8 citations)
    Psyche-R1 is a Chinese psychological LLM that combines three abilities usually built separately: empathetic counselling, psychology domain knowledge, and explicit reasoning.
  3. Add-One-In: Incremental Sample Selection for Large Language Models via a Choice-Based Greedy Paradigm (2025 · 5 citations)
    Picking a compact, high-value subset of instruction data can cut supervised fine-tuning (SFT) costs for LLMs.
  4. PsychEthicsBench: Evaluating Large Language Models Against Australian Mental Health Ethics (2026 · 2 citations)
    Most safety evaluations of LLMs judge a model by whether it refuses harmful requests. In mental health this is a weak measure. A curt refusal can seem cold, discourage someone from seeking help, or still contain ethically problematic content. Existing medical-ethics benchmarks also tend to assume one shared standard…

All works

5 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. Self-Instructed Derived Prompt Generation Meets In-Context Learning: Unlocking New Potential of Black-Box LLMsAnnual Meeting of the Association for Computational Linguistics (ACL)Zhuo Li, Yuhao Du, Jinpeng Hu, et al.2025 · 9 citations
  2. Psyche-R1: Towards Reliable Psychological LLMs through Unified Empathy, Expertise, and ReasoningAnnual Meeting of the Association for Computational Linguistics (ACL)Chongyuan Dai, Jinpeng Hu, Hongchang Shi, et al.2026 · 8 citations
  3. Add-One-In: Incremental Sample Selection for Large Language Models via a Choice-Based Greedy ParadigmConference on Empirical Methods in Natural Language Processing (EMNLP)Zhuo Li, Yuhao Du, Xiaoqi Jiao, et al.2025 · 5 citations
  4. PsychEthicsBench: Evaluating Large Language Models Against Australian Mental Health EthicsFindings of the Association for Computational LinguisticsYaling Shen, Stephanie Fong, Yiwen Jiang, et al.2026 · 2 citations
  5. WebUncertainty: Dual-Level Uncertainty Driven Planning and Reasoning For Autonomous Web Agent ↗Findings of the Association for Computational Linguistics: ACL 2026 · no summary yetLingfeng Zhang et al.2026

 Co-authors here

12 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 18 times (of the 149,439 papers whose bibliographies we read); 6 citing papers with a page here →.

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