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Deepak Nathani

Deepak Nathani has 2 papers in our library, in 2019–2024, most of them on Language models & pretraining, Knowledge graphs & ontologies; often with Liangming Pan, Wenda Xu, William Yang Wang.

2 papers in our library · 2019–2024

OpenAlex · h-index 7 · 24 works · 837 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 168 citations in our library · 3 more in our library without a page yet · ⭐ Paper Star rank #4433

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 California, Santa BarbaraSanta Barbara · United States2023–2026 · 9 paperscurrent🎓Indian Institute of Technology HyderabadSangāreddi · India2018–2020 · 6 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 2 more institutions on single bylines are not listed.

 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📚 Shelves 5 Arts and Humanities · 2 works Computer Science · 2 works Artificial Intelligence · 2 works Language and Linguistics · 2 works Information Systems · 1 work

🧭 Topics 2 Language models & pretraining · 1 work Knowledge graphs & ontologies · 1 work

 Works by year

2020s

20241 paper

2010s

20191 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Automatically Correcting Large Language Models: Surveying the Landscape of Diverse Automated Correction Strategies (2024 · 140 citations)
    Large language models still make characteristic mistakes. They invent facts, reach conclusions that do not follow from their own reasoning, and sometimes produce toxic or biased text. Fixing these flaws with human feedback (e.g., RLHF) is expensive and cannot happen in real time. This TACL survey therefore focuses on…
  2. Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs (2019 · 108 citations)
    Knowledge graphs are often incomplete, so relation prediction (knowledge base completion) aims to decide whether a missing triple is valid.

All works

5 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. Automatically Correcting Large Language Models: Surveying the Landscape of Diverse Automated Correction StrategiesLiangming Pan, Michael Saxon, Wenda Xu, et al.Transactions of the Association for… · 140 citations
  2. Learning Attention-based Embeddings for Relation Prediction in Knowledge GraphsDeepak Nathani, Jatin Chauhan, Charu Sharma, et al.Annual Meeting of the Association for… · 108 citations
  3. MLGym: A New Framework and Benchmark for Advancing AI Research Agents (original ↗)Deepak Nathani et al.2025 · arXiv:2502.14499 · 42 citations · no summary yet
  4. Few-shot Controllable Style Transfer for Low-Resource Multilingual Settings (original ↗)Kalpesh Krishna et al.2022 · Proceedings of the 60th Annual Meeting of the… · 35 citations · no summary yet
  5. WildSci: Advancing Scientific Reasoning from In-the-Wild Literature (original ↗)Tengxiao Liu et al.2026 · Findings of the Association for Computational… · no summary yet

 Co-authors here

4 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 168 times (of the 141,840 papers whose bibliographies we read); 7 citing papers with a page here →.

Updated

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