🧑‍🔬 Olivier Delalleau

🏛️ Affiliations

🎓Université de MontréalMontreal · Canada2003–2012 · 16 papers🔬Centre de recherches mathématiquesMontreal · Canada2004–2006 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 7 more institutions on single bylines are not listed.

🧭 Fields, topics and concepts

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

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. HelpSteer2: Open-source dataset for training top-performing reward models (2024 · 82 citations)
   Reward models that steer LLMs toward human-preferred outputs need good preference data. Openly licensed collections such as HH-RLHF, Open Assistant and HelpSteer have aged, and sets distilled from GPT-4 and similar proprietary systems carry commercial-use restrictions. The NVIDIA authors address this gap with…
2. HelpSteer: Multi-attribute Helpfulness Dataset for SteerLM (2024 · 72 citations)
   Open helpfulness preference datasets usually give only an overall rating or a pairwise ranking. They do not say why one answer is better, so trained models can pick up shallow cues, such as preferring longer replies whether or not they are useful. The NVIDIA team built HelpSteer to address this. It is a…

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. HelpSteer2: Open-source dataset for training top-performing reward modelsarXiv:2406.08673Zhilin Wang, Yi Dong, Olivier Delalleau, et al.2024 · 82 citations
2. HelpSteer: Multi-attribute Helpfulness Dataset for SteerLMConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Zhilin Wang, Yi Dong, Jiaqi Zeng, et al.2024 · 72 citations
3. HelpSteer2-Preference: Complementing Ratings with Preferences ↗arXiv:2410.01257 · no summary yetZhilin Wang et al.2024 · 57 citations
4. Diverging Preferences: When do Annotators Disagree and do Models Know? ↗arXiv (Cornell University) · no summary yetMichael JQ Zhang et al.2024 · 16 citations

🤝 Co-authors here

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

• Daniel Egert ×2
• Jiaqi Zeng ×2
• Makesh Narsimhan Sreedhar ×2
• Oleksii Kuchaiev ×2
• Yi Dong ×2
• Zhilin Wang ×2
• Aidan Swope
• Gerald Shen
• Neel Kant
• Virginia Adams

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• Conference of the North American Chapter of the Association for Computational Linguistics (NAACL): NAACL 2024

🔗 Cited by

Papers of our library cite their works 459 times (of the 149,439 papers whose bibliographies we read); 122 citing papers with a page here →.

Updated 2 November 2026

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