Mitigating Large Language Model Hallucinations via Autonomous Knowledge Graph-based Retrofitting

Incorporating factual knowledge in knowledge graph is regarded as a promising approach for mitigating the hallucination of large language models (LLMs). Existing methods usually only use the user's input to query the knowledge graph, thus failing to address the factual hallucination generated by LLMs during its reasoning process. To address this problem, this paper proposes Knowledge Graph-based Retrofitting (KGR), a new framework that incorporates LLMs with KGs to mitigate factual hallucination during the reasoning process by retrofitting the initial draft responses of LLMs based on the factual knowledge stored in KGs. Specifically, KGR leverages LLMs to extract, select, validate, and retrofit factual statements within the model-generated responses, which enables an autonomous knowledge verifying and refining procedure without any additional manual efforts. Experiments show that KGR can significantly improve the performance of LLMs on factual QA benchmarks especially when involving complex reasoning processes, which demonstrates the necessity and effectiveness of KGR in mitigating hallucination and enhancing the reliability of LLMs

SenseFi: A library and benchmark on deep-learning-empowered WiFi human sensing

Over the recent years, WiFi sensing has been rapidly developed for privacy-preserving, ubiquitous human-sensing applications, enabled by signal processing and deep-learning methods. However, a comprehensive public benchmark for deep learning in WiFi sensing, similar to that available for visual recognition, does not yet exist. In this article, we review recent progress in topics ranging from WiFi hardware platforms to sensing algorithms and propose a new library with a comprehensive benchmark, SenseFi. On this basis, we evaluate various deep-learning models in terms of distinct sensing tasks, WiFi platforms, recognition accuracy, model size, computational complexity, and feature transferability. Extensive experiments are performed whose results provide valuable insights into model design, learning strategy, and training techniques for real-world applications. In summary, SenseFi is a comprehensive benchmark with an open-source library for deep learning in WiFi sensing research that offers researchers a convenient tool to validate learning-based WiFi-sensing methods on multiple datasets and platforms.Nanyang Technological UniversityPublished versionThis research is supported by NTU Presidential Postdoctoral Fellowship, ‘‘Adaptive Multi-modal Learning for Robust Sensing and Recognition in Smart Cities’’ project fund (020977-00001), at the Nanyang Technological University, Singapore

Assimilation of FY-4A GIIRS radiance observations in the forecast of Typhoon Bavi

We assimilated radiance observations from the Geostationary Interferometric Infrared Sounder (GIIRS) onboard the FengYun-4A geostationary satellite to evaluate their impact on the forecast of Typhoon Bavi using WRFDA. The temperature channels with high information content, representing 90% of the information content of all temperature channels, were selected for assimilation. All radiance observations above the cloud-top were assimilated by comparing the channel height to the cloud-top height coming from the product of the Advanced Geosynchronous Radiation Imager (AGRI). The assimilation of the GIIRS observations decreased the root-mean-square error of the temperature by 2% and improved the precipitation forecast. The rain band in southeast China was reproduced well, thus showing that infrared hyperspectral radiance observations have added value in improving the circulation around typhoons and, therefore, provide better forecasts. The increased relative humidity in the upper layer and stronger typhoon outflow were found to related to the intensify of typhoon in the analysis compared with the control experiment

MM-Fi: Multi-Modal Non-Intrusive 4D Human Dataset for Versatile Wireless Sensing

4D human perception plays an essential role in a myriad of applications, such as home automation and metaverse avatar simulation. However, existing solutions which mainly rely on cameras and wearable devices are either privacy intrusive or inconvenient to use. To address these issues, wireless sensing has emerged as a promising alternative, leveraging LiDAR, mmWave radar, and WiFi signals for device-free human sensing. In this paper, we propose MM-Fi, the first multi-modal non-intrusive 4D human dataset with 27 daily or rehabilitation action categories, to bridge the gap between wireless sensing and high-level human perception tasks. MM-Fi consists of over 320k synchronized frames of five modalities from 40 human subjects. Various annotations are provided to support potential sensing tasks, e.g., human pose estimation and action recognition. Extensive experiments have been conducted to compare the sensing capacity of each or several modalities in terms of multiple tasks. We envision that MM-Fi can contribute to wireless sensing research with respect to action recognition, human pose estimation, multi-modal learning, cross-modal supervision, and interdisciplinary healthcare research.Comment: The paper has been accepted by NeurIPS 2023 Datasets and Benchmarks Track. Project page: https://ntu-aiot-lab.github.io/mm-f

A Transgenic Mouse Model for DNA/RNA Gene Therapy of Human β Thalassemia

TheâIVS-2-654 C→T mutation accounts for approximately 20% of â thalassemia mutation in southern  China; it causes aberrant RNA splicing and leads to â thalassemia. To provide an animal model for testing  therapies for correcting splicing defects, we have produced two lines of transgenic mice with the human â  thalassemia mutant gene. The transgenic mice carrying this mutant gene show the same aberrant splicing  as their human counterparts and provide an animal model for testing therapies to correct splicing defects  at either the RNA or DNA level.