The relatively recent public release of generative artificial intelligence (AI) systems has ignited a significant leap in awareness of the capabilities of AI. In parallel, there has been a recognition of AI system lim...
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Sign language is a vital communication medium enabling individuals with speech and hearing impairments to convey thoughts, feelings, and ideas effectively. However, the limited understanding of sign language, outside ...
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The Resonant Object Interface (ROI) is an acoustic input system that provides nuanced, embodied, and expressive engagement with control signals in audio software. The ROI’s unique sensing methodology is built from re...
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In order to improve the performance of Web service recommendation in small world heterogeneous network, this paper proposes a method based on reputation evaluation. The method defines the calculation method of reputat...
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Cognitive logic gives artificial intelligent agent the formal expression, but as its base, modal logic with common sense understanding and its standard semantics, can seemly cause logic omniscient problems. There are ...
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In the ever-expanding realm of wildlife conservation and ecological research, the use of automated image classification software has emerged as a valuable tool for extracting crucial insights from camera trap images. ...
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Based on the security of distributed machine learning data and model based on remote proof, a cross-domain dynamic remote proof scheme based on ring signature improves the efficiency of computer cluster proof, the app...
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Log parsing is defined as the process of extracting structured information from unstructured log data. It is an important step prior to many log analytics tasks. The emergence of Large Language Models (LLMs), like Gen...
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ISBN:
(纸本)9798350395693;9798350395686
Log parsing is defined as the process of extracting structured information from unstructured log data. It is an important step prior to many log analytics tasks. The emergence of Large Language Models (LLMs), like Generative Pre-trained Transformers (GPTs), has driven the development of novel log parsing methods. Existing studies have examined the effectiveness of large-scale general-purpose LLMs in log parsing. In this paper, we argue that the long-term adoption of such LLMs pose challenges of data privacy, cost, and tool integration. To address these challenges, we explore the viability of supervised fine-tuning of an open-source compact LLM for log parsing as a prospective alternative. To this end, we fine-tune the Mistral-7B-Instruct LLM on a diverse set of log files and evaluate its performance, in terms of both accuracy and robustness, against OpenAI's GPT-4-Turbo using different configuration settings. We apply two evaluation approaches, namely metric-based and LLM-based. Our overall findings show that fine-tuning a compact LLM such as Mistral-7B provides similar and sometimes better results than using a large-scale LLM, in our case GPT-4-Turbo. These findings are important because they enable companies to use a smaller LLM that they can readily adapt to parsing their log data, and integrate into their log analytics tools, without the need to rely on third-party LLM providers.
Virtual reality (VR) offers unprecedented user experiences across diverse areas. However, few studies have focused on code clone in VR software, though it is a significant factor in the emergence and spread of securit...
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The Industrial Internet of Things (IIoT) involves numerous industrial systems and equipment. Due to challenges in node data processing, a software-defined network (SDN) can improve IIoT control and management. However...
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