Papers by Arash Ahmadian
RLHF Can Speak Many Languages: Unlocking Multilingual Preference Optimization for LLMs (2024.emnlp-main)
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| Challenge: | Preference optimization is a widely adopted post-training technique to align large language models with human preferences. |
| Approach: | They propose a method for generating multilingual feedback data to balance data coverage. |
| Outcome: | The proposed method achieves 54.4% win-rate against current state-of-the-art multilingual LLM in its parameter class and 69.5% win- rate or higher against widely used models like Gemma, Mistral and Llama 3. |
Back to Basics: Revisiting REINFORCE-Style Optimization for Learning from Human Feedback in LLMs (2024.acl-long)
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Arash Ahmadian, Chris Cremer, Matthias Gallé, Marzieh Fadaee, Julia Kreutzer, Olivier Pietquin, Ahmet Üstün, Sara Hooker
| Challenge: | Proximal Policy Optimization (PPO) is used for RLHF but requires high computational cost and sensitive hyperparameter tuning. |
| Approach: | They propose to use Proximal Policy Optimization to align large language models to human preferences. |
| Outcome: | The proposed method preserves and even increases performance while preserving the motivational principles that led to the development of PPO. |
The Multilingual Alignment Prism: Aligning Global and Local Preferences to Reduce Harm (2024.emnlp-main)
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null Aakanksha, Arash Ahmadian, Beyza Ermis, Seraphina Goldfarb-Tarrant, Julia Kreutzer, Marzieh Fadaee, Sara Hooker
| Challenge: | Existing approaches to safety alignment focus on homogeneous monolingual settings . preference training and safety measures often overfit to harms common in Western-centric datasets . |
| Approach: | They propose to use human annotated red teaming prompts to identify global and local harms. |
| Outcome: | The proposed approach can address and optimize for a non-homogeneous set of languages and cultural preferences while minimizing both global and local harms. |
Contrastive Policy Gradient: Aligning LLMs on sequence-level scores in a supervised-friendly fashion (2024.emnlp-main)
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Yannis Flet-Berliac, Nathan Grinsztajn, Florian Strub, Eugene Choi, Bill Wu, Chris Cremer, Arash Ahmadian, Yash Chandak, Mohammad Azar, Olivier Pietquin, Matthieu Geist
| Challenge: | Reinforcement Learning (RL) is a method used to fine tune Large Language Models (LLMs) using a reward model trained from preference data to better align with human judgment. |
| Approach: | They propose a Reinforcement Learning (RL) algorithm that can estimate the optimal policy even from off-policy data. |
| Outcome: | The proposed algorithm can estimate the optimal policy even from off-policy data. |