Neural knowledge-to-text generation models often struggle to faithfully generate descriptions for the input facts: they may produce hallucinations that contradict the given facts, or describe facts not present in the input. To reduce hallucinations, we propose a novel decoding method, TWEAK (Think While Effectively Articulating Knowledge). TWEAK treats the generated sequences at each decoding step… →
On-device machine learning (ML) moves computation from the cloud to personal devices, protecting user privacy and enabling intelligent user experiences. However, fitting models on devices with limited resources presents a major technical challenge: practitioners need to optimize models and balance hardware metrics such as model size, latency, and power. To help practitioners create efficient ML… →
The reproducibility and transparency of large language models are crucial for advancing open research, ensuring the trustworthiness of results, and enabling investigations into data and model biases, as well as potential risks. To this end, we release OpenELM, a state-of-the-art open language model. OpenELM uses a layer-wise scaling strategy to efficiently allocate parameters within each… →
Extracting information quickly and efficiently from websites and digital documents is crucial for businesses, researchers, and developers. They require specific data from various online sources to analyze trends, monitor competitors, or gather insights for strategic decisions. Collecting this data can be time-consuming and prone to errors, presenting a significant challenge in data-driven industries. Traditionally, web… →
Edge artificial intelligence (Edge AI) involves implementing AI algorithms and models on local devices like sensors or IoT devices at the network’s periphery. This setup allows for immediate data processing and analysis, reducing dependence on cloud infrastructure. Consequently, it empowers devices to make intelligent decisions quickly and autonomously without the need for data from distant… →
Large Language Models (LLMs) are advancing at a very fast pace in recent times. However, the lack of adequate data to thoroughly verify particular features of these models is one of the main obstacles. An additional layer of complication arises when evaluating the precision and caliber of a model’s free-form text production on its own. … →
CONCLUSIONS: A study with good evidence and clear outcomes in children with CP might open an avenue for the potential best use of neurostimulation. →
CONCLUSIONS: The present microbiological results, which are related to short-term outcomes up to 6 months post-treatment, support the adjunctive subgingival application of sodium hypochlorite/amino acids and xHyA to subgingival debridement in the treatment of periodontitis. →
CONCLUSIONS: At-home skin temperature monitoring for diabetes-related foot ulcer prevention compared with usual care is at best equally cost-effective. The intervention resulted in cost-savings due to preventing foot ulcer recurrence and related costs, but this came at the expense of QALY loss, potentially from self-monitoring burdens. →