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Sachin Kumar

Sachin Kumar has 4 papers in our library, in 2023–2024, most of them on Language models & pretraining, Text generation, Bias, fairness & safety; often with Yulia Tsvetkov, Yejin Choi, Alane Suhr.

4 papers in our library · 2023–2024

OpenAlex · ORCID · h-index 37 · 211 works · 5,945 citations OpenAlex, 2026-10-14

📄 4 works in our library · 🔗 312 citations in our library · 3 more in our library without a page yet · ⭐ Paper Star rank #3687

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

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 42 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.

 Works by year

2020s

 Papers

The most cited first (OpenAlex and our library).

  1. Do All Languages Cost the Same? Tokenization in the Era of Commercial Language Models (2023 · 164 citations)
    Commercial LM APIs bill per token, but how many tokens a text needs depends on the tokenizer and its training data.
  2. WildTeaming at Scale: From In-the-Wild Jailbreaks to (Adversarially) Safer Language Models (2024 · 89 citations)
    Frontier LLMs can still be jailbroken, and open safety-training data is scarce, since even open-weight models rarely publish theirs.
  3. Language Generation Models Can Cause Harm: So What Can We Do About It? An Actionable Survey (2023 · 87 citations)
    Much has been written about the dangers of large text generators, but practical ways to counter them were scattered across separate research communities.
  4. Minding Language Models' (Lack of) Theory of Mind: A Plug-and-Play Multi-Character Belief Tracker (2023 · 74 citations)
    Large language models still handle theory of mind (ToM) poorly. They struggle to track what each character in a story believes, especially when a belief is false, and what one character assumes about another's beliefs. Earlier fixes trained models on templated benchmarks, and those models tend to exploit dataset…

All works

7 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. MAGNET: Improving the Multilingual Fairness of Language Models with Adaptive Gradient-Based Tokenization (original ↗)Orevaoghene Ahia et al.2024 · arXiv:2407.08818 · 16 citations · no summary yet
  2. FLEXITOKENS: Flexible Tokenization for Evolving Language Models (original ↗)Abraham Toluwase Owodunni, Orevaoghene Ahia, Sachin Kumar2026 · Findings of the Association for Computational… · 2 citations · no summary yet
  3. Reasoning Up the Instruction Ladder for Controllable Language Models (original ↗)Zishuo Zheng et al.2026 · Findings of the Association for Computational… · 1 citation · no summary yet

 Co-authors here

12 of 20 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 312 times (of the 141,840 papers whose bibliographies we read); 27 citing papers with a page here →.

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