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Yuelin Bai

Yuelin Bai has 2 papers in our library, in 2024–2025, most of them on Language models & pretraining, Reasoning, Information retrieval & search; often with Bo Li, Graham Neubig, Min Yang.

2 papers in our library · 2024–2025

OpenAlex · ORCID · Wikidata · h-index 5 · 19 works · 83 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 208 times in our library · 4 more in our library without a page yet · ⭐ Paper Stars ranking: #6623

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

🔬Shenzhen Institutes of Advanced TechnologyShenzhen · China2024–2026 · 8 paperscurrent🏛️Chinese Academy of SciencesBeijing · China2024–2026 · 7 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 6The field of science this work belongs to (OpenAlex fields).Arts and Humanities · 2 worksComputer Science · 2 worksArtificial Intelligence · 2 worksLanguage and Linguistics · 2 worksInformation Systems · 1 workComputer Vision and Pattern Recognition · 1 work

🧭 Topics 4Research subjects and languages the work is about.Language models & pretraining · 2 worksInformation retrieval & search · 1 workReasoning · 1 workVision & language · 1 work

 Works by year

2020s

20251 paper20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Enhancing Noise Robustness of Retrieval-Augmented Language Models with Adaptive Adversarial Training (2024 · 75 citations)
    Retrieval-augmented language models (RALMs) often go wrong when the retriever returns unhelpful or misleading passages.
  2. 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…

All works

6 works of theirs in our library, the most cited first; 2 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. Enhancing Noise Robustness of Retrieval-Augmented Language Models with Adaptive Adversarial TrainingAnnual Meeting of the Association for Computational Linguistics (ACL)Feiteng Fang, Yuelin Bai, Shiwen Ni, et al.2024 · 75 citations
  3. MAP-Neo: Highly Capable and Transparent Bilingual Large Language Model Series ↗arXiv:2405.19327 · no summary yetGe Zhang et al.2024 · 44 citations
  4. 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
  5. DeliLaw: A Chinese Legal Counselling System Based on a Large Language Model ↗arXiv:2408.00357 · no summary yetNan Xie et al.2024 · 8 citations
  6. COIG-P: A High-Quality and Large-Scale Chinese Preference Dataset for Alignment with Human Values ↗Findings of the Association for Computational Linguistics: EACL 2026 · no summary yetSiwei Wu et al.2026

 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 208 times (of the 149,439 papers whose bibliographies we read); 74 citing papers with a page here →.

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