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相关论文: A Cost-Benefit Analysis of On-Premise Large Langua…

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The rapid proliferation of Large Language Models presents both opportunities and challenges for the translation field. While commercial, cloud-based AI chatbots have garnered significant attention in translation studies, concerns regarding…

计算与语言 · 计算机科学 2025-08-01 Peter Sandrini

This paper explores the economic underpinnings of open sourcing advanced large language models (LLMs) by for-profit companies. Empirical analysis reveals that: (1) LLMs are compatible with R&D portfolios of numerous technologically…

综合经济学 · 经济学 2025-01-22 Mahyar Habibi

Large language models have gained widespread popularity for their ability to process natural language inputs and generate insights derived from their training data, nearing the qualities of true artificial intelligence. This advancement has…

软件工程 · 计算机科学 2024-11-25 Narcisa Guran , Florian Knauf , Man Ngo , Stefan Petrescu , Jan S. Rellermeyer

Many companies use large language models (LLMs) offered as a service, like OpenAI's GPT-4, to create AI-enabled product experiences. Along with the benefits of ease-of-use and shortened time-to-solution, this reliance on proprietary…

Large Language Models (LLMs) have become central in academia and industry, raising concerns about privacy, transparency, and misuse. A key issue is the trustworthiness of proprietary models, with open-sourcing often proposed as a solution.…

软件工程 · 计算机科学 2025-01-29 Domen Vake , Bogdan Šinik , Jernej Vičič , Aleksandar Tošić

The rapid rise of Large Language Models (LLMs) has revolutionized various artificial intelligence (AI) applications, from natural language processing to code generation. However, the computational demands of these models, particularly in…

Context: Large Language Models (LLMs) enable automation of complex natural language processing across domains, but research on domain-specific applications like Finance remains limited. Objectives: This study explored open-source and…

软件工程 · 计算机科学 2025-09-30 Theo Koraag , Niklas Wagner , Felix Dobslaw , Lucas Gren

Contacting customer service via chat is a common practice. Because employing customer service agents is expensive, many companies are turning to NLP that assists human agents by auto-generating responses that can be used directly or with…

The advent of large language models (LLMs) revolutionized natural language processing applications, and running LLMs on edge devices has become increasingly attractive for reasons including reduced latency, data localization, and…

计算与语言 · 计算机科学 2024-09-17 Jiajun Xu , Zhiyuan Li , Wei Chen , Qun Wang , Xin Gao , Qi Cai , Ziyuan Ling

Large language models (LLMs) are widely applied in chatbots, code generators, and search engines. Workload such as chain-of-throught, complex reasoning, agent services significantly increase the inference cost by invoke the model…

计算与语言 · 计算机科学 2025-11-27 Sihyeong Park , Sungryeol Jeon , Chaelyn Lee , Seokhun Jeon , Byung-Soo Kim , Jemin Lee

Recent advances in large language models (LLMs) have dramatically improved performance on a wide range of tasks, driving rapid enterprise adoption. Yet, the cost of adopting these AI services is understudied. Unlike traditional software…

计算机与社会 · 计算机科学 2025-11-18 Soogand Alavi , Salar Nozari , Andrea Luangrath

Large Language Models (LLMs) deliver powerful AI capabilities but face deployment challenges due to high resource costs and latency, whereas Small Language Models (SLMs) offer efficiency and deployability at the cost of reduced performance.…

人工智能 · 计算机科学 2025-05-13 Yi Chen , JiaHao Zhao , HaoHao Han

This paper conceptualizes Large Language Models (LLMs) as a form of mixed public goods within digital infrastructure, analyzing their economic properties through a comprehensive theoretical framework. We develop mathematical models to…

计算机与社会 · 计算机科学 2025-09-17 Yukun Zhang , TianYang Zhang

The potential of large language models (LLMs) in specialized domains such as legal risk analysis remains underexplored. In response to growing interest in locally deploying open-source LLMs for legal tasks while preserving data…

人工智能 · 计算机科学 2025-08-06 Shuang Liu , Zelong Li , Ruoyun Ma , Haiyan Zhao , Mengnan Du

Large Language Models (LLMs) have emerged as transformative tools for natural language understanding and user intent resolution, enabling tasks such as translation, summarization, and, increasingly, the orchestration of complex workflows.…

软件工程 · 计算机科学 2025-11-12 Justus Flerlage , Alexander Acker , Odej Kao

Large language models (LLMs) have achieved remarkable progress across domains and applications but face challenges such as high fine-tuning costs, inference latency, limited edge deployability, and reliability concerns. Small language…

计算与语言 · 计算机科学 2025-11-06 Fali Wang , Jihai Chen , Shuhua Yang , Ali Al-Lawati , Linli Tang , Hui Liu , Suhang Wang

Despite speculation that recent large language models (LLMs) are likely to be used maliciously to improve the quality or scale of influence operations, uncertainty persists regarding the economic value that LLMs offer propagandists. This…

计算机与社会 · 计算机科学 2023-08-08 Micah Musser

Large Language Models (LLMs) are distinguished by their architecture, which dictates their parameter size and performance capabilities. Social scientists have increasingly adopted LLMs for text classification tasks, which are difficult to…

计算与语言 · 计算机科学 2024-11-05 Marcello Carammia , Stefano Maria Iacus , Giuseppe Porro

State-of-the-art large language models require specialized hardware and substantial energy to operate. As a consequence, cloud-based services that provide access to large language models have become very popular. In these services, the…

计算机科学与博弈论 · 计算机科学 2026-05-29 Ander Artola Velasco , Stratis Tsirtsis , Nastaran Okati , Manuel Gomez-Rodriguez

Large language models (LLMs) are transforming society, powering applications from smartphone assistants to autonomous driving. Yet cloud-based LLM services alone cannot serve a growing class of applications, including those operating under…

信号处理 · 电气工程与系统科学 2026-05-12 Liangqi Yuan , Wenzhi Fang , Shiqiang Wang , H. Vincent Poor , Christopher G. Brinton
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