Bi-Level Contextual Bandits for Individualized Resource Allocation under Delayed Feedback

Large language models (LLMs) are known for their impressive text generation capabilities; however, they frequently produce factually incorrect content, a phenomenon referred to as hallucination. This issue is particularly concerning in critical fields such as healthcare and finance. Traditional methods for detecting these inaccuracies often require multiple API calls, leading to increased latency and costs. The introduction of CONFACTCHECK offers a new approach that checks for consistency in responses to factual queries, enhancing the reliability of LLM outputs without needing external knowled…

A new framework for summarizing consumer health questions (CHQs) has been proposed, aiming to improve communication in healthcare. This framework integrates TextRank-based sentence extraction and medical named entity recognition with large language models (LLMs). Experiments with the LLaMA-2-7B model on the MeQSum and BanglaCHQ-Summ datasets showed significant improvements in quality and faithfulness metrics, with over 80% of summaries preserving critical medical information. This highlights the importance of faithfulness in medical summarization.

A recent study published on arXiv investigates the use of neural networks for biometric authentication through finger-drawn digits on touchscreen devices. The research involved twenty participants who contributed a total of 2,000 finger-drawn digits. Two CNN architectures were evaluated, achieving approximately 89% authentication accuracy, while autoencoder approaches reached about 75% accuracy. The findings suggest that this method offers a secure and user-friendly biometric solution that can be integrated with existing authentication systems.

The article presents a new method for computing tomographic projections of a 3D volume using a linear combination of shifted B-splines. This method employs a ray-tracing algorithm to calculate 3D line integrals with various projection geometries. A neural network is integrated into the algorithm to efficiently compute the contributions of the basis functions, resulting in higher reconstruction quality compared to traditional voxel-based methods.