21 citations · 81 across the 37 of their papers we have counts for
4 papers · 1 filter
SparsePO: Controlling Preference Alignment of LLMs via Sparse Token Masks
Fenia Christopoulou, Ronald Cardenas, Gerasimos Lampouras +2
Direct alignment algorithms have proven an effective step for aligning language models to human-desired behaviors. Current variants of the Direct Preference Optimization objective…
Mixture of Attentions For Speculative Decoding
Matthieu Zimmer, Milan Gritta, Gerasimos Lampouras +2
The growth in the number of parameters of Large Language Models (LLMs) has led to a significant surge in computational requirements, making them challenging and costly to deploy. S…
Group Robust Preference Optimization in Reward-free RLHF
Shyam Sundhar Ramesh, Yifan Hu, Iason Chaimalas +4
Adapting large language models (LLMs) for specific tasks usually involves fine-tuning through reinforcement learning with human feedback (RLHF) on preference data. While these data…
Why Can Large Language Models Generate Correct Chain-of-Thoughts?
Rasul Tutunov, Antoine Grosnit, Juliusz Ziomek +2
This paper delves into the capabilities of large language models (LLMs), specifically focusing on advancing the theoretical comprehension of chain-of-thought prompting. We investig…