70 citations · 80 across the 8 of their papers we have counts for
8 papers
AuPair: Golden Example Pairs for Code Repair
Aditi Mavalankar, Hassan Mansoor, Zita Marinho +2
Scaling up inference-time compute has proven to be a valuable strategy in improving the performance of Large Language Models (LLMs) without fine-tuning. An important task that can…
VQA Training Sets are Self-play Environments for Generating Few-shot Pools
Tautvydas Misiunas, Hassan Mansoor, Jasper Uijlings +2
Large-language models and large-vision models are increasingly capable of solving compositional reasoning tasks, as measured by breakthroughs in visual-question answering benchmark…
Chart-based Reasoning: Transferring Capabilities from LLMs to VLMs
Victor Carbune, Hassan Mansoor, Fangyu Liu +4
Vision-language models (VLMs) are achieving increasingly strong performance on multimodal tasks. However, reasoning capabilities remain limited particularly for smaller VLMs, while…
Parameter Efficient Reinforcement Learning from Human Feedback
Hakim Sidahmed, Samrat Phatale, Alex Hutcheson +16
While Reinforcement Learning from Human Feedback (RLHF) effectively aligns pretrained Large Language and Vision-Language Models (LLMs, and VLMs) with human preferences, its computa…
ScreenAI: A Vision-Language Model for UI and Infographics Understanding
Gilles Baechler, Srinivas Sunkara, Maria Wang +7
Screen user interfaces (UIs) and infographics, sharing similar visual language and design principles, play important roles in human communication and human-machine interaction. We…
The Impact of Preference Agreement in Reinforcement Learning from Human Feedback: A Case Study in Summarization
Sian Gooding, Hassan Mansoor
Reinforcement Learning from Human Feedback (RLHF) can be used to capture complex and nuanced properties of text generation quality. As a result, the task of text summarization has…
LLMs cannot find reasoning errors, but can correct them given the error location
Gladys Tyen, Hassan Mansoor, Victor Cărbune +2
While self-correction has shown promise in improving LLM outputs in terms of style and quality (e.g. Chen et al., 2023b; Madaan et al., 2023), recent attempts to self-correct logic…
RLAIF vs. RLHF: Scaling Reinforcement Learning from Human Feedback with AI Feedback
Harrison Lee, Samrat Phatale, Hassan Mansoor +8
Reinforcement learning from human feedback (RLHF) has proven effective in aligning large language models (LLMs) with human preferences, but gathering high-quality preference labels…