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Daniel Kang

Daniel Kang has 2 papers in our library, in 2020–2024, most of them on Text generation; often with Tatsunori Hashimoto.

2 papers in our library · 2020–2024

OpenAlex · ORCID · Wikidata · h-index 23 · 70 works · 2,154 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 140 times in our library · 4 more in our library without a page yet · ⭐ Paper Stars ranking: #4118

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 Illinois Urbana-ChampaignUrbana · United States2022–2026 · 39 papersEmployment since 2023 · Assistant Professor, Computer Sciencecurrent🎓Stanford UniversityStanford · United States2016–2024 · 19 papersEducation 2016–2023🎓Massachusetts Institute of TechnologyCambridge · United States2011–2017 · 5 papersEducation 2011–2015🔬MIT Computer Science and Artificial Intelligence LaboratoryCambridge · United States2016–2017 · 2 papers🎓University of California, BerkeleyBerkeley · United States2022–2023Employment 2022–2023 · Postdoc, Computer Science🎓University of CambridgeCambridge · United Kingdom2015–2016Education 2015–2016

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 4 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 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 1Research subjects and languages the work is about.Text generation · 1 work

 Works by year

2020s

20241 paperInjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents20201 paperImproved Natural Language Generation via Loss Truncation

 Papers

The most cited first (OpenAlex and our library).

  1. InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model Agents (2024 · 187 citations)
    LLM agents that call tools often read outside content such as emails, reviews, notes or web pages. An attacker can plant instructions in that content to steer the agent against its own user. This is called indirect prompt injection (IPI). InjecAgent is presented as the first benchmark that measures how vulnerable…
  2. Improved Natural Language Generation via Loss Truncation (2020 · 129 citations)
    Conditional text generators are usually trained with log loss (KL divergence). This objective forces the model to cover every reference in the training set, so a small share of noisy or invalid targets can distort what it learns. Examples include titles containing facts absent from the source. Kang and Hashimoto…

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. InjecAgent: Benchmarking Indirect Prompt Injections in Tool-Integrated Large Language Model AgentsFindings of the Association for Computational LinguisticsQiusi Zhan, Zhixiang Liang, Zifan Ying, et al.2024 · 187 citations
  2. Improved Natural Language Generation via Loss TruncationAnnual Meeting of the Association for Computational Linguistics (ACL)Daniel Kang, Tatsunori B. Hashimoto2020 · 129 citations
  3. Teams of LLM Agents can Exploit Zero-Day VulnerabilitiesProceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers) · original ↗ · no summary yetYuxuan Zhu et al.2026 · 16 citations
  4. Dias: Dynamic Rewriting of Pandas CodearXiv:2303.16146 · original ↗ · no summary yetStefanos Baziotis, Daniel Kang, Charith Mendis2024 · 9 citations
  5. Holistic Agent Leaderboard: The Missing Infrastructure for AI Agent EvaluationarXiv:2510.11977 · original ↗ · no summary yetSayash Kapoor et al.2025 · 8 citations
  6. SafeSearch: Do Not Trade Safety for Utility in LLM Search AgentsFindings of the Association for Computational Linguistics: EACL 2026 · original ↗ · no summary yetQiusi Zhan et al.2026 · 1 citation

 Co-authors here

1 co-author 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 140 times (of the 141,840 papers whose bibliographies we read); 15 citing papers with a page here →.

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