USC researchers present Safer-Instruct: a new methodology to automatically build large-scale preference data
Alignment of language models is very important, particularly in a subset of RLHF methods that have been applied to strengthen ...
Tag: Researchers
Meta AI and NYU researchers propose E-RLHF to combat LLM jailbreaking
Large language models (LLMs) have gained prominence in deep learning, demonstrating exceptional capabilities in several domains such as assistance, code ...
aiXplain researchers develop innovative approaches for fast instruction tracking in Arabic with LLM
Large language models require large datasets of prompts paired with particular user requests and correct answers for training purposes. Large ...
Apple researchers present KGLens: a new artificial intelligence method designed to visualize and evaluate the factual knowledge embedded in LLMs
Large language models (LLMs) have gained significant attention for their versatility, but their veracity remains a critical concern. Studies have ...
GPS spoofers 'hack time' on commercial airlines, researchers say By Reuters
By James Pearson LAS VEGAS (Reuters) - A recent surge in “GPS spoofing,” a form of digital attack that can ...
Researchers at New York University have open-sourced GPUDrive: a GPU-accelerated multi-agent driving simulation at 1 million FPS
Multi-agent planning for mixed human-robot environments faces significant challenges. Current methodologies, which often rely on data-driven human motion prediction and ...
Belgian researchers discover a huge privacy hole in six dating apps
TechnologyCrunch A group of researchers from KU Leuven University in Belgium identified six popular dating apps that malicious users can ...
CMU researchers explore expert guidance and strategic deviations in multi-agent imitation learning
The problem of a mediator learning to coordinate a group of strategic agents is posed through recommendations of actions without ...
Microsoft and Stanford University researchers present Trace: an innovative Python framework poised to revolutionize automatic optimization of AI systems
Designing computational workflows for ai applications such as chatbots and coding assistants is complex due to the need to manage ...
Stanford researchers present contrastive preference learning (CPL): a new machine learning framework for RLHF that uses the regret preference model
Aligning models with human preferences poses significant challenges in ai research, particularly in sequential and high-dimensional decision-making tasks. Traditional reinforcement ...
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