Papers by Nathan Lambert
RewardBench: Evaluating Reward Models for Language Modeling (2025.findings-naacl)
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Nathan Lambert, Valentina Pyatkin, Jacob Morrison, Lester James Validad Miranda, Bill Yuchen Lin, Khyathi Chandu, Nouha Dziri, Sachin Kumar, Tom Zick, Yejin Choi, Noah A. Smith, Hannaneh Hajishirzi
| Challenge: | Evaluating reward models presents an opportunity to understand the opaque technologies used for alignment of language models. |
| Approach: | They present a benchmark dataset and code-base for evaluation of reward models . they use prompt-chosen-rejected trios to benchmark how they perform on queries . |
| Outcome: | The proposed dataset compares RMs with other models on a set of questions. |
M-RewardBench: Evaluating Reward Models in Multilingual Settings (2025.acl-long)
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Srishti Gureja, Lester James Validad Miranda, Shayekh Bin Islam, Rishabh Maheshwary, Drishti Sharma, Gusti Triandi Winata, Nathan Lambert, Sebastian Ruder, Sara Hooker, Marzieh Fadaee
| Challenge: | Reward models (RMs) are primarily trained and evaluated in English and their capabilities in multilingual settings remain understudied. |
| Approach: | They construct a multilingual RM evaluation benchmark that tests the chat, safety, reasoning, and translation capabilities of RMs in 23 languages. |
| Outcome: | The proposed model performs better for high-resource languages and improves with translation quality. |