Elamur Research⌕
☰
home / authors / Ruixiang Tang

Ruixiang Tang

Ruixiang Tang has 4 papers in our library, in 2023–2026, most of them on Language models & pretraining, Reasoning, Vision & language; often with Xia Hu, Andrew Wen, Bing Yin.

4 papers in our library · 2023–2026

OpenAlex · ORCID · Wikidata · h-index 12 · 54 works · 1,425 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 Cited 242 times in our library · 5 more in our library without a page yet · ⭐ Paper Stars ranking: #2680

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

🎓Rutgers, The State University of New JerseyNew Brunswick · United States2024–2026 · 12 papersEmployment since 2024 · Assistant Professor, Computer Sciencecurrent🎓Rochester Institute of TechnologyRochester · United States2026 · 1 papercurrent🎓Rice UniversityHouston · United States2020–2024 · 24 papersEducation 2021–2024 · PhD, Computer Science🎓Texas A&M UniversityCollege Station · United States2019–2024 · 7 papersEducation 2019–2021 · Computer Science and Engineering🎓Tsinghua UniversityBeijing · China2019–2020 · 3 papers🎓Duke UniversityDurham · United States2019 · 2 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 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 · 3 works

🧭 Topics 3Research subjects and languages the work is about.Language models & pretraining · 4 worksReasoning · 1 workVision & language · 1 work

 Works by year

2020s

20261 paper20252 papers20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond (2023 · 814 citations)
    This survey is a practitioner-oriented guide to deciding when large language models are the right tool for downstream NLP work, and when they are not. The authors do not train anything new. Instead, they organise existing knowledge along three axes: models, data and tasks.
  2. Disentangling Memory and Reasoning Ability in Large Language Models (2025 · 45 citations)
    Large language models typically answer knowledge-heavy questions with one undivided chain of thought. This makes it hard to tell whether a wrong answer comes from missing facts or from faulty logic, and the blending is associated with knowledge being lost mid-chain and with hallucination.
  3. TACO: Enhancing Multimodal In-context Learning via Task Mapping-Guided Sequence Configuration (2025 · 22 citations)
    Large vision–language models (LVLMs) can learn tasks from interleaved image–text demonstrations, but their in-context learning (ICL) is fragile. Small changes to the chosen demonstrations (ICDs) can noticeably shift outputs, and existing selection methods rely on hand-crafted relevance metrics.
  4. FaithLM: Towards Faithful Explanations for Large Language Models (2026 · 9 citations)
    LLMs often explain their answers in natural language, but these explanations frequently do not reflect what actually drove the prediction. Classic fidelity tests, which delete important tokens, also fit poorly with free-text rationales whose content is semantic rather than token-level.

All works

9 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. Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and BeyondACM Transactions on Knowledge Discovery from DataJingfeng Yang, Hongye Jin, Ruixiang Tang, et al.2023 · 814 citations
  2. Disentangling Memory and Reasoning Ability in Large Language ModelsAnnual Meeting of the Association for Computational Linguistics (ACL)Mingyu Jin, Weidi Luo, Sitao Cheng, et al.2025 · 45 citations
  3. TACO: Enhancing Multimodal In-context Learning via Task Mapping-Guided Sequence ConfigurationConference on Empirical Methods in Natural Language Processing (EMNLP)Yanshu Li, Jianjiang Yang, Tian Yun, et al.2025 · 22 citations
  4. FaithLM: Towards Faithful Explanations for Large Language ModelsConference of the European Chapter of the Association for Computational Linguistics (EACL)Yu-Neng Chuang, Guanchu Wang, Chia-Yuan Chang, et al.2026 · 9 citations
  5. Counting Circuits: Mechanistic Interpretability of Visual Reasoning in Large Vision-Language Models ↗arXiv (Cornell University) · no summary yetLiwei Che et al.2026 · 2 citations
  6. Learning from Primitive: Probing Visual Reasoning of LVLMs via Counting ↗Lecture notes in computer science · no summary yetLiwei Che et al.2026
  7. Why MLLMs Struggle to Count: Overcoming Individuation and Aggregation Bottlenecks with ConvStack ↗arXiv (Cornell University) · no summary yetLiwei Che et al.2026
  8. Reasoning over Precedents Alongside Statutes: Case-Augmented Deliberative Alignment for LLM Safety ↗Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetCan Jin et al.2026
  9. A Decoupled Multi-Agent Framework for Complex Text Style Transfer ↗Findings of the Association for Computational Linguistics: EMNLP 2025 · no summary yetLingxi Zhang et al.2025

 Co-authors here

12 of 23 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 242 times (of the 149,439 papers whose bibliographies we read); 39 citing papers with a page here →.

Updated

↑ back to top

This page in Markdown → https://md.research.elamur.ai/free/en/authors/ruixiang-tang/