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Vijay Viswanathan

Vijay Viswanathan has 3 papers in our library, in 2024–2025, most of them on Language models & pretraining, Evaluation & benchmarks; often with Graham Neubig, Carolin Lawrence, Kiril Gashteovski.

3 papers in our library · 2024–2025

OpenAlex · h-index 5 · 19 works · 136 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 167 times in our library · 9 more in our library without a page yet · ⭐ Paper Stars ranking: #5860

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

🎓Carnegie Mellon UniversityPittsburgh · United States2021–2026 · 23 paperscurrent🎓Fudan UniversityShanghai · China2022 · 2 papers🎓National University of SingaporeSingapore · Singapore2022 · 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 6The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 3 worksArts and Humanities · 3 worksComputer Science · 3 worksLanguage and Linguistics · 3 worksGeneral Social Sciences · 1 workSocial Sciences · 1 work

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 3 worksEvaluation & benchmarks · 1 work

 Works by year

2020s

20252 papers20241 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Large Language Models Enable Few-Shot Clustering (2024 · 111 citations)
    Clustering without supervision is underspecified, because the algorithm cannot know which grouping the user actually wants.
  2. Checklists Are Better Than Reward Models For Aligning Language Models (2025 · 37 citations)
    Reinforcement learning works well for tasks with checkable answers. It is much harder to apply to open-ended instruction following, where reward models can be gamed and generic criteria such as "helpfulness" miss request-specific needs. The authors propose Reinforcement Learning from Checklist Feedback (RLCF), which…
  3. Evaluating Language Models as Synthetic Data Generators (2025 · 32 citations)
    Synthetic data now drives much of language model post-training. Earlier data-generation studies, however, each used different generator LMs, data volumes, student models and benchmarks, so their results cannot be compared to show which LM produces the most useful training data. Kim et al. address this with…

All works

12 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. Large Language Models Enable Few-Shot ClusteringTransactions of the Association for Computational Linguistics (TACL)Vijay Viswanathan, Kiril Gashteovski, Carolin Lawrence, et al.2024 · 111 citations
  2. Checklists Are Better Than Reward Models For Aligning Language ModelsConference on Neural Information Processing Systems (NeurIPS)Vijay Viswanathan, Yanchao Sun, Shuang Ma, et al.2025 · 37 citations
  3. Evaluating Language Models as Synthetic Data GeneratorsAnnual Meeting of the Association for Computational Linguistics (ACL)Seungone Kim, Juyoung Suk, Xiang Yue, et al.2025 · 32 citations
  4. Prompt2Model: Generating Deployable Models from Natural Language Instructions ↗Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations · no summary yetVijay Viswanathan et al.2023 · 30 citations
  5. DataFinder: Scientific Dataset Recommendation from Natural Language Descriptions ↗Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetVijay Viswanathan et al.2023 · 26 citations
  6. DataLab: A Platform for Data Analysis and Intervention ↗Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations · no summary yetYang Xiao et al.2022 · 19 citations
  7. SELF-GUIDE: Better Task-Specific Instruction Following via Self-Synthetic Finetuning ↗arXiv (Cornell University) · no summary yetChenyang Zhao et al.2024 · 15 citations
  8. Synthetic Multimodal Question Generation ↗Findings of the Association for Computational Linguistics: EMNLP 2024 · no summary yetIan Wu et al.2024 · 13 citations
  9. Synthetic Data in the Era of Large Language Models ↗Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 5: Tutorial Abstracts) · no summary yetVijay Viswanathan et al.2025 · 6 citations
  10. Training Task Experts through Retrieval Based Distillation ↗arXiv (Cornell University) · no summary yetJiaxin Ge et al.2024 · 1 citation
  11. Discretizing Reward Models ↗arXiv (Cornell University) · no summary yetVijay Viswanathan et al.2026
  12. ClusterFusion: Hybrid Clustering with Embedding Guidance and LLM Adaptation ↗arXiv (Cornell University) · no summary yetYiming Xu et al.2025

 Co-authors here

12 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 167 times (of the 149,439 papers whose bibliographies we read); 33 citing papers with a page here →.

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