Post-training quantization reduces the computational demand of Large Language Models (LLMs) but can weaken some of their capabilities. Since LLM abilities emerge with scale, smaller LLMs are more sensitive to quantization. In this paper, we explore how quantization affects smaller LLMs’ ability to perform retrieval-augmented generation (RAG), specifically in longer contexts. We chose personalization for evaluation because it is a challenging domain to perform using RAG as it requires long-context reasoning over multiple documents. We compare the original FP16 and the quantized INT4 performance of multiple 7B and 8B LLMs on two tasks while progressively increasing the number of retrieved documents to test how quantized models fare against longer contexts. To better understand the effect of retrieval, we evaluate three retrieval models in our experiments. Our findings reveal that if a 7B LLM performs the task well, quantization does not impair its performance and long-context reasoning capabilities. We conclude that it is possible to utilize RAG with quantized smaller LLMs.
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Introduction: Visuospatial neglect (VSN) is common after stroke and can seriously hamper everyday life. One of the most commonly used and highly recommended rehabilitation methods is Visual Scanning Training (VST) which requires a lot of repetition which makes the treatment intensive and less appealing for the patient. The use of eHealth in healthcare can increase options regarding improved treatment in the areas of patient satisfaction, treatment efficacy and effectiveness. One solution to motivational issues might be Augmented Reality (AR), which offers new opportunities for increasing natural interactions with the environment during treatment of VSN. Aim: The development of an AR-based scanning training program that will improve visuospatial search strategies in individuals affected by VSN. Method: We used a Design Research approach, which is characterized by the iterative and incremental use of prototypes as research instruments together with a strong human-centered focus. Several design thinking methods were used to explore which design elements the AR game should comply with. Seven patients with visuospatial neglect, eight occupational therapists, a game design professional and seven other healthcare professionals participated in this research by means of co-creation based on their own perspectives. Results: Fundamental design choices for an AR game for VSN patients included the factors extrinsic motivation, nostalgia, metaphors, direct feedback, independent movement, object contrast, search elements and competition. Designing for extrinsic motivation was considered the most important design choice, because due to less self-awareness the target group often does not fully understand and accept the consequences of VSN. Conclusion: This study produced a prototype AR game for people with VSN after stroke. The AR game and method used illustrate the promising role of AR tools in geriatric rehabilitation, specifically those aimed at increasing the independence of patients with VSN after stroke. 2020 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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Dealing with and maintaining high-quality standards in the design and construction phases is challenging, especially for on-site construction. Issues like improper implementation of building components and poor communication can widen the gap between design specifications and actual conditions. To prevent this, particularly for energy-efficient buildings, it is vital to develop resilient, sustainable strategies. These should optimize resource use, minimize environmental impact, and enhance livability, contributing to carbon neutrality by 2050 and climate change mitigation. Traditional post-occupancy evaluations, which identify defects after construction, are impractical for addressing energy performance gaps. A new, real-time inspection approach is necessary throughout the construction process. This paper suggests an innovative guideline for prefabricated buildings, emphasizing digital ‘self-instruction’ and ‘self-inspection’. These procedures ensure activities impacting quality adhere to specific instructions, drawings, and 3D models, incorporating the relevant acceptance criteria to verify completion. This methodology, promoting alignment with planned energy-efficient features, is supported by BIM-based software and Augmented Reality (AR) tools, embodying Industry 4.0 principles. BIM (Building Information Modeling) and AR bridge the gap between virtual design and actual construction, improving stakeholder communication and enabling real-time monitoring and adjustments. This integration fosters accuracy and efficiency, which are key for energy-efficient and nearly zero-energy buildings, marking a shift towards a more precise, collaborative, and environmentally sensible construction industry.
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