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Shashwat Goel

Shashwat Goel has 2 papers in our library, in 2023–2024, most of them on Evaluation & benchmarks; often with Alexander Pan, Ann-Kathrin Dombrowski, Long Phan.

2 papers in our library · 2023–2024

OpenAlex · h-index 4 · 19 works · 151 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 462 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #4155

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

🔬Max Planck Institute for Intelligent SystemsStuttgart · Germany2024–2026 · 5 paperscurrent🔬ELLIS Institute TübingenTübingen · Germany2024–2026 · 4 paperscurrent🔬Max Planck SocietyMunich · Germany2024–2026 · 2 paperscurrent🎓International Institute of Information Technology, HyderabadHyderabad · India2022–2024 · 4 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksComputer Science · 2 worksSafety Research · 2 worksSocial Sciences · 2 works

🧭 Topics 1Research subjects and languages the work is about.Evaluation & benchmarks · 1 work

 Works by year

2020s

20241 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Representation Engineering: A Top-Down Approach to AI Transparency (2023 · 397 citations)
    Interpretability methods that analyse individual neurons and circuits have trouble explaining the complex behaviour of large language models.
  2. The WMDP Benchmark: Measuring and Reducing Malicious Use With Unlearning (2024 · 127 citations)
    Governments and AI labs worry that large language models could help bad actors build biological, chemical or cyber weapons.

All works

4 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. Representation Engineering: A Top-Down Approach to AI TransparencyarXiv:2310.01405Andy Zou, Long Phan, Sarah Chen, et al.2023 · 397 citations
  2. The WMDP Benchmark: Measuring and Reducing Malicious Use With UnlearningarXiv:2403.03218Nathaniel Li, Alexander Pan, Anjali Gopal, et al.2024 · 127 citations
  3. Rethinking Rubric Generation for Improving LLM Judge and Reward Modeling for Open-ended Tasks ↗arXiv:2602.05125 · no summary yetWilliam F. Shen et al.2026 · 9 citations
  4. Training AI Co-Scientists Using Rubric Rewards ↗arXiv:2512.23707 · no summary yetShashwat Goel et al.2025 · 6 citations

 Co-authors here

8 co-authors with a page, the most papers together first.

 Cited by

Papers of our library cite their works 462 times (of the 149,439 papers whose bibliographies we read); 139 citing papers with a page here →.

Updated

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