Empower Large Language Model to Perform Better on Industrial Domain-Specific Question Answering

Large Language Model (LLM) has gained popularity and achieved remarkable results in open-domain tasks, but its performance in real industrial domain-specific scenarios is average since there is no specific knowledge in it. This issue has attracted widespread attention, but there are few relevant benchmarks available. In this paper, we provide a benchmark Question Answering (QA) dataset named MSQA, which is about Microsoft products and IT technical problems encountered by customers. This dataset contains industry cloud-specific QA knowledge, which is not available for general LLM, so it is well suited for evaluating methods aimed at improving domain-specific capabilities of LLM. In addition, we propose a new model interaction paradigm that can empower LLM to achieve better performance on domain-specific tasks where it is not proficient. Extensive experiments demonstrate that the approach following our model fusion framework outperforms the commonly used LLM with retrieval methods.

Can Connected Autonomous Vehicles really improve mixed traffic efficiency in realistic scenarios?

Connected autonomous vehicles (CAVs) can supplement the information from their own sensors with information from surrounding CAVs for decision making and control. This has the potential to improve traffic efficiency. CAVs face additional challenges in their driving, however, when they interact with human-driven vehicles (HDVs) in mixed-traffic environments due to the uncertainty in human's driving behavior e.g. larger reaction times, perception errors, etc. While a lot of research has investigated the impact of CAVs on traffic safety and efficiency at different penetration rates, all have assumed either perfect communication or very simple scenarios with imperfect communication. In practice, the presence of communication delays and packet losses means that CAVs might receive only partial information from surrounding vehicles, and this can have detrimental effects on their performance. This paper investigates the impact of CAVs on traffic efficiency in realistic communication and road network scenarios (i.e. imperfect communication and large-scale road network). We analyze the effect of unreliable communication links on CAVs operation in mixed traffic with various penetration rates and evaluate traffic performance in congested traffic scenarios on a large-scale road network (the M50 motorway, in Ireland). Results show that CAVs can significantly improve traffic efficiency in congested traffic scenarios at high penetration rates. The scale of the improvement depends on communication reliability, with a packet drop rate of 70% leading to an increase in traffic congestion by 28.7% and 11.88% at 40% and 70% penetration rates respectively compared to perfect communication.