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Original Paper: https://arxiv.org/abs/2308.01990 By: Rodrigo Pedro, Daniel Castro, Paulo Carreira, Nuno Santos Abstract: Large Language Models (LLMs) have found widespread applications in various domains, including web applications, where they facilitate human interaction via chatbots with natural language interfaces. Internally, aided by an LLM-integration middleware such
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Original Paper: https://arxiv.org/abs/2308.07411 By: Edward Junprung Abstract: The final frontier for simulation is the accurate representation of complex, real-world social systems. While agent-based modeling (ABM) seeks to study the behavior and interactions of agents within a larger system, it is unable to faithfully capture the
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Original Paper: https://arxiv.org/abs/2308.06721 By: Hu Ye, Jun Zhang, Sibo Liu, Xiao Han, Wei Yang Abstract: Recent years have witnessed the strong power of large text-to-image diffusion models for the impressive generative capability to create high-fidelity images. However, it is very tricky to generate desired images
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Original Paper: https://arxiv.org/abs/2308.05596 By: Xinlei He, Savvas Zannettou, Yun Shen, Yang Zhang Abstract: The spread of toxic content online is an important problem that has adverse effects on user experience online and in our society at large. Motivated by the importance and impact of the
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Original Paper: https://arxiv.org/abs/2308.04138 By: Dietrich Trautmann Abstract: Prompting is used to guide or steer a language model in generating an appropriate response that is consistent with the desired outcome. Chaining is a strategy used to decompose complex tasks into smaller, manageable components. In this study,
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Original Paper: https://arxiv.org/abs/2307.07415 By: Hong Sun, Xue Li, Yinchuan Xu, Youkow Homma, Qi Cao, Min Wu, Jian Jiao, Denis Charles Abstract: This paper presents AutoHint, a novel framework for automatic prompt engineering and optimization for Large Language Models (LLM). While LLMs have demonstrated remarkable ability
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Original Paper: https://arxiv.org/abs/2305.02897 By: Konstantin Hebenstreit, Robert Praas, Louis P Kiesewetter, Matthias Samwald Abstract: Emergent chain-of-thought (CoT) reasoning capabilities promise to improve performance and explainability of large language models (LLMs). However, uncertainties remain about how reasoning strategies formulated for previous model generations generalize to new
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Original Paper: https://arxiv.org/abs/1706.03762 By: Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, Illia Polosukhin Abstract: The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. Thebest-performingg models also connect the
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Original Paper: https://arxiv.org/abs/2308.01472 By: Florinel-Alin Croitoru, Vlad Hondru, Radu Tudor Ionescu, Mubarak Shah Abstract: Text-to-image diffusion models such as Stable Diffusion have recently attracted the interest of many researchers, and inverting the diffusion process can play an important role in better understanding the generative process
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Original Paper: https://arxiv.org/abs/2307.16364 By: Paul Denny, Juho Leinonen, James Prather, Andrew Luxton-Reilly, Thezyrie Amarouche, Brett A. Becker, Brent N. Reeves Abstract: With their remarkable ability to generate code, large language models (LLMs) are a transformative technology for computing education practice. They have created an urgent
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Original Paper: https://arxiv.org/abs/2307.15337 By: Xuefei Ning, Zinan Lin, Zixuan Zhou, Zifu Wang, Huazhong Yang, Yu Wang Abstract: This work aims at decreasing the end-to-end generation latency of large language models (LLMs). One of the major causes of the high generation latency is the sequential decoding
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Original Paper: https://arxiv.org/abs/2307.12980 By: Jindong Gu, Zhen Han, Shuo Chen, Ahmad Beirami, Bailan He, Gengyuan Zhang, Ruotong Liao, Yao Qin, Volker Tresp, Philip Torr Abstract: Prompt engineering is a technique that involves augmenting a large pre-trained model with task-specific hints, known as prompts, to adapt