A Large Language Model (LLM) is an advanced type of artificial intelligence designed to understand and generate human-like text. It’s trained on vast amounts of data, enabling it to perform various natural language processing tasks, such as answering questions, summarizing content, and engaging in conversation. LLMs are revolutionizing education by serving as chatbots that enrich… →
Language model alignment is quite important, particularly in a subset of methods from RLHF that have been applied to strengthen the safety and competence of AI systems. Language models are deployed in many applications today, and their outputs can be harmful or biased. Inherent human preference alignment under RLHF ensures that their behaviors are ethical… →
Future reward estimation is crucial in RL as it predicts the cumulative rewards an agent might receive, typically through Q-value or state-value functions. However, these scalar outputs lack detail about when or what specific rewards the agent anticipates. This limitation is significant in applications where human collaboration and explainability are essential. For instance, in a… →
Vision-language models (VLMs) have gained significant attention due to their ability to handle various multimodal tasks. However, the rapid proliferation of benchmarks for evaluating these models has created a complex and fragmented landscape. This situation poses several challenges for researchers. Implementing protocols for numerous benchmarks is time-consuming, and interpreting results across multiple evaluation metrics becomes… →
Large Language Models (LLMs) have gained prominence in deep learning, demonstrating exceptional capabilities across various domains such as assistance, code generation, healthcare, and theorem proving. The training process for LLMs typically involves two stages: pretraining with massive corpora and an alignment step using Reinforcement Learning from Human Feedback (RLHF). However, LLMs need help generating appropriate… →
CONCLUSION: The applied active AE assessment procedure provides a comprehensive AE assessment tool that can be used by others-with adaptations for the specific context, if needed. Based on our experiences, we outline recommendations for future studies. →
We assessed whether multiplex real-time PCR plus conventional microbiological testing is safe and more effective than conventional microbiological testing alone for reducing antibiotic use in community-acquired pneumonia (CAP). In this randomised trial, we recruited adults hospitalised with CAP at four Spanish hospitals. Patients were randomly assigned (1:1) to undergo either multiplex real-time PCR in non-invasive… →
CONCLUSION: Patients with diabetes and malnutrition in the hospital setting have a particularly high risk for adverse outcomes and mortality. Individualised nutritional support reduced mortality in this secondary analysis of a randomized trial, but this effect was not significant calling for further large-scale trials in this vhighly ulnerable patient population. →
Extended Reality (XR) technology transforms how users interact with digital environments, blending the physical and virtual worlds to create immersive experiences. XR devices are equipped with advanced sensors that capture rich streams of user data, enabling personalized and context-aware interactions. The rapid evolution of this field has prompted researchers to explore the integration of artificial… →
Language models (LMs) exhibit improved performance with increased size and training data, yet the relationship between model scale and hallucinations remains unexplored. Defining hallucinations in LMs presents challenges due to their varied manifestations. A new study from Google Deepmind focuses on hallucinations where correct answers appear verbatim in training data. Achieving low hallucination rates demands… →