Papers by David Rosenberg
Domain Generalizable AI Guardrails with Augmented Policy Training (2026.acl-long)
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| Challenge: | Current guardrails overfit the training policies, preventing adaptation to new domains and policies. |
| Approach: | They propose a training recipe that uses a suite of policy perturbation strategies to reduce overfitting and increase generalization to guardrails. |
| Outcome: | The proposed training recipe reduces overfitting and increases generalization on unseen policies and achieves comparable or better performance than existing 8B guardrails on unsen policies. |
Academics Can Contribute to Domain-Specialized Language Models (2024.emnlp-main)
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Mark Dredze, Genta Winata, Prabhanjan Kambadur, Shijie Wu, Ozan Irsoy, Steven Lu, Vadim Dabravolski, David Rosenberg, Sebastian Gehrmann
| Challenge: | Commercially available models dominate academic leaderboards, focusing on creating and adapting general-purpose models . however, general- purpose models often underperform in specialized domains, and domain-specific models yield superior results. |
| Approach: | They advocate for a renewed focus on developing and evaluating domain- and task-specific models . they advocate for an adapted or adapted model that can be used to improve academic leaderboard standings . |
| Outcome: | The proposed model can do well on professional and linguistic examinations, college-level knowledge questions, and collections of reasoning tasks. |
MixCE: Training Autoregressive Language Models by Mixing Forward and Reverse Cross-Entropies (2023.acl-long)
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| Challenge: | Autoregressive language models are trained by minimizing the cross-entropy of the model distribution Q relative to the data distribution P. However, these systems still struggle in many openended generation settings, where they are asked to produce a long text following a short prompt. |
| Approach: | They propose to combine forward and reverse cross-entropy to train autoregressive language models by minimizing the cross-Entropy of the model distribution Q relative to the data distribution P. |
| Outcome: | The proposed model overgeneralizes and produces non-human-like text without complex decoding strategies. |