Papers by Olivier Delalleau
HelpSteer3: Human-Annotated Feedback and Edit Data to Empower Inference-Time Scaling in Open-Ended General-Domain Tasks (2025.acl-long)
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Zhilin Wang, Jiaqi Zeng, Olivier Delalleau, Daniel Egert, Ellie Evans, Hoo-Chang Shin, Felipe Soares, Yi Dong, Oleksii Kuchaiev
| Challenge: | Inference-Time Scaling is critical to the success of recent models such as OpenAI o1 and DeepSeek R1 . however, many techniques require tasks to have answers that can be verified . |
| Approach: | They use data to train dedicated Feedback and Edit Models capable of inference-time scaling for open-ended tasks. |
| Outcome: | The proposed model can reach SoTA performance on Arena Hard at 92.7 as of 5 Mar 2025. |
HelpSteer: Multi-attribute Helpfulness Dataset for SteerLM (2024.naacl-long)
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Zhilin Wang, Yi Dong, Jiaqi Zeng, Virginia Adams, Makesh Narsimhan Sreedhar, Daniel Egert, Olivier Delalleau, Jane Scowcroft, Neel Kant, Aidan Swope, Oleksii Kuchaiev
| Challenge: | Existing helpfulness preference datasets do not specify what makes some responses more helpful and others less helpful. |
| Approach: | They use a dataset that has annotated for correctness, coherence, complexity, and verbosity. |
| Outcome: | The dataset has annotations for correctness, coherence, complexity, and verbosity in addition to overall helpfulness of responses. |