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Yilin Niu

Yilin Niu has 4 papers in our library, in 2024–2026, most of them on Language models & pretraining; often with Hongning Wang, Minlie Huang, Aohan Zeng.

4 papers in our library · 2024–2026

OpenAlex · h-index 2 · 5 works · 184 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 542 times in our library · ⭐ Paper Stars ranking: #8093

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

🏢Zhipu AIBeijing · China2024–2025 · 3 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.

 Fields, topics and concepts

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

📚 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 1Research subjects and languages the work is about.Language models & pretraining · 3 works

 Works by year

2020s

20262 papers20251 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. LongReward: Improving Long-context Large Language Models with AI Feedback (2025 · 21 citations)
    Long-context LLMs are usually fine-tuned on question-answer data that other LLMs generate automatically. That data often contains logical slips, invented facts or incomplete answers, and these flaws carry over into the SFT models. Reinforcement learning could correct such behaviour, but there has been no dependable…
  2. ChatGLM-RLHF: Practices of Aligning Large Language Models with Human Feedback (2024 · 17 citations)
    This paper from Zhipu AI and Tsinghua University describes how the team built and ran the RLHF pipeline used in production for the ChatGLM assistant, with particular attention to Chinese.
  3. IF-CRITIC: Towards a Fine-Grained LLM Critic for Instruction-Following Evaluation (2026 · 1 citation)
    LLM-as-a-Judge is often used to supply rewards for training instruction-following. It has two problems. First, it is expensive, because strong proprietary or reasoning models must judge every constraint in a separate call. Second, it is unreliable: these judges miss errors and count poorly. IF-CRITIC is a 14B critic…
  4. IF-RewardBench: Benchmarking Judge Models for Instruction-Following Evaluation (2026 · 1 citation)
    Judge models (LLM-as-a-Judge and reward models) are widely used to check whether LLM outputs obey instructions and to supply reward signals for alignment.

 Co-authors here

12 of 22 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 542 times (of the 149,439 papers whose bibliographies we read); 129 citing papers with a page here →.

Updated

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