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Jinjie Ni

Jinjie Ni has 4 papers in our library, in 2023–2026, most of them on Language models & pretraining, Reasoning, Evaluation & benchmarks; often with Fuzhao Xue, Yang You, Erik Cambria.

4 papers in our library · 2023–2026

OpenAlex · h-index 8 · 26 works · 488 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 147 times in our library · 1 more in our library without a page yet · ⭐ Paper Stars ranking: #6297

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

🎓National University of SingaporeSingapore · Singapore2024–2026 · 6 paperscurrent🎓Nanyang Technological UniversitySingapore · Singapore2022–2023 · 10 papers🏢Alibaba GroupHangzhou · China2023 · 2 papers🎓Northwestern Polytechnical UniversityXi'an · China2019 · 2 papers

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 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 · 2 works

🧭 Topics 4Research subjects and languages the work is about.Language models & pretraining · 2 worksReasoning · 2 worksEvaluation & benchmarks · 1 workKnowledge graphs & ontologies · 1 work

 Works by year

2020s

20261 paper20242 papers20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. OpenMoE: An Early Effort on Open Mixture-of-Experts Language Models (2024 · 64 citations)
    Before this work, few open Mixture-of-Experts (MoE) language models had been trained on trillion-scale, diverse data, and little was known about how their routers actually behave.
  2. MixEval: Deriving Wisdom of the Crowd from LLM Benchmark Mixtures (2024 · 47 citations)
    Evaluating LLMs reliably is difficult. Closed-answer test sets such as MMLU poorly reflect what real users ask. LLM-judged suites like MT-Bench inherit judge biases and contain few queries. Static sets leak into training data over time, and crowd-voting platforms like Chatbot Arena are slow, costly and hard to…
  3. Logical Reasoning over Natural Language as Knowledge Representation: A Survey (2023 · 27 citations)
    Classic AI approaches to logical reasoning store knowledge in formal languages such as first-order logic and rely on symbolic solvers.
  4. SynthRL: Scaling Visual Reasoning with Verifiable Data Synthesis (2026 · 3 citations)
    SynthRL tackles a data bottleneck in reinforcement learning with verifiable rewards (RLVR) for vision-language models. Curated question–answer pairs are costly, and data selection alone cannot go beyond the pool that already exists. The authors instead generate new training items. Their pipeline has three steps:

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. OpenMoE: An Early Effort on Open Mixture-of-Experts Language ModelsarXiv:2402.01739Fuzhao Xue, Zian Zheng, Yao Fu, et al.2024 · 64 citations
  2. MixEval: Deriving Wisdom of the Crowd from LLM Benchmark MixturesConference on Neural Information Processing Systems (NeurIPS)Jinjie Ni, Fuzhao Xue, Xiang Yue, et al.2024 · 47 citations
  3. Logical Reasoning over Natural Language as Knowledge Representation: A SurveyarXiv:2303.12023Zonglin Yang, Xinya Du, Rui Mao, et al.2023 · 27 citations
  4. ResearchBench: Benchmarking LLMs in Scientific Discovery via Inspiration-Based Task Decomposition ↗arXiv:2503.21248 · no summary yetYujie Liu et al.2025 · 25 citations
  5. SynthRL: Scaling Visual Reasoning with Verifiable Data SynthesisFindings of the Association for Computational LinguisticsZijian Wu, Jinjie Ni, Xiangyan Liu, et al.2026 · 3 citations

 Co-authors here

12 of 14 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 147 times (of the 149,439 papers whose bibliographies we read); 33 citing papers with a page here →.

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