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Yizhi Li

Yizhi Li has 3 papers in our library, in 2024–2025, most of them on Language models & pretraining, Reasoning, Vision & language; often with Anne Lauscher, Bo Li, Brigitte Krenn.

3 papers in our library · 2024–2025

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

📄 3 works in our library · 🔗 Cited 191 times in our library · 4 more in our library without a page yet · ⭐ Paper Stars ranking: #7106

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

🎓University of ManchesterManchester · United Kingdom2023–2026 · 45 paperscurrent🎓University of SheffieldSheffield · United Kingdom2022–2023 · 10 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 3 more institutions on single bylines are not listed.

 Fields, topics and concepts

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

📚 Shelves 7The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 3 worksComputer Science · 3 worksArtificial Intelligence · 3 worksComputer Vision and Pattern Recognition · 3 worksLanguage and Linguistics · 3 worksPsychology · 1 workExperimental and Cognitive Psychology · 1 work

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 2 worksReasoning · 1 workVision & language · 1 work

 Works by year

2020s

20252 papers20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. MAmmoTH-VL: Eliciting Multimodal Reasoning with Instruction Tuning at Scale (2025 · 40 citations)
    Open-source multimodal LLMs tend to struggle with complex reasoning. A major reason is that their instruction-tuning data is mostly recycled from academic VQA-style sets such as VQA, AI2D, and ChartQA, which offer short answers and no step-by-step justification. The authors propose a low-cost pipeline built entirely…
  2. Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation (2025 · 25 citations)
    The survey looks at how large (multimodal) language models are starting to reshape scientific work. Earlier reviews tended to cover single disciplines or a few tasks. This paper instead follows the research lifecycle from start to finish. It covers five stages: finding and summarizing literature, generating ideas and…
  3. OmniBench: Towards The Future of Universal Omni-Language Models (2024 · 17 citations)
    Most multimodal LLMs are built and tested on pairs of modalities (image–text or audio–text), so it is unclear how well they handle an image, an audio clip and a text instruction together.

All works

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

  1. SuperGPQA: Scaling LLM Evaluation across 285 Graduate Disciplines ↗arXiv:2502.14739 · no summary yetM-A-P Team et al.2025 · 89 citations
  2. MAP-Neo: Highly Capable and Transparent Bilingual Large Language Model Series ↗arXiv:2405.19327 · no summary yetGe Zhang et al.2024 · 44 citations
  3. MAmmoTH-VL: Eliciting Multimodal Reasoning with Instruction Tuning at ScaleAnnual Meeting of the Association for Computational Linguistics (ACL)Jiawei Guo, Tianyu Zheng, Yizhi Li, et al.2025 · 40 citations
  4. Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and EvaluationACM Computing SurveysSteffen Eger, Yong Cao, Jennifer D'Souza, et al.2025 · 25 citations
  5. OmniBench: Towards The Future of Universal Omni-Language ModelsConference on Neural Information Processing Systems (NeurIPS)Yizhi Li, Yinghao Ma, Ge Zhang, et al.2024 · 17 citations
  6. MMRA: A Benchmark for Evaluating Multi-Granularity and Multi-Image Relational Association Capabilities in Large Visual Language Models ↗Findings of the Association for Computational Linguistics: EACL 2026 · no summary yetSiwei Wu et al.2026
  7. LoopCoder: Scaling Code Intelligence via Looped Language Models ↗Findings of the Association for Computational Linguistics: ACL 2026 · no summary yetJian Yang et al.2026

 Co-authors here

12 of 24 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 191 times (of the 149,439 papers whose bibliographies we read); 69 citing papers with a page here →.

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