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Zhuo Li

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

4 papers in our library · 2025–2026

OpenAlex · h-index 0 · 4 works · 0 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 17 times in our library · ⭐ Paper Stars ranking: #8912

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

🎓Chinese University of Hong Kong, ShenzhenShenzhen · China2026 · 2 paperscurrent🏢Alibaba GroupHangzhou · China2026 · 1 papercurrent🎓Harbin Institute of TechnologyHarbin · China2026 · 1 papercurrent

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 6The 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 · 1 workMedicine · 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. Safeguarding LLM Fine-tuning via Push-Pull Distributional Alignment (2026)
    Fine-tuning a safety-aligned LLM on user data can weaken its refusal behaviour, even when the data looks harmless.

 Co-authors here

10 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 17 times (of the 149,439 papers whose bibliographies we read); 5 citing papers with a page here →.

Updated

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