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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/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
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Original Paper: https://arxiv.org/abs/2307.09909 By: Urszula Jessen, Michal Sroka, Dirk Fahland Abstract: This research investigates the application of Large Language Models (LLMs) to augment conversational agents in process mining, aiming to tackle its inherent complexity and diverse skill requirements. While LLM advancements present novel opportunities for
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Original Paper: https://arxiv.org/abs/2307.01142 By: Stephen MacNeil, Andrew Tran, Joanne Kim, Ziheng Huang, Seth Bernstein, Dan Mogil Abstract: To help users do complex work, researchers have developed techniques to integrate AI and human intelligence into user interfaces (UIs). With the recent introduction of large language models
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Original Paper: https://arxiv.org/abs/2306.06211 By: Chaoning Zhang, Fachrina Dewi Puspitasari, Sheng Zheng, Chenghao Li, Yu Qiao, Taegoo Kang, Xinru Shan, Chenshuang Zhang, Caiyan Qin, Francois Rameau, Lik-Hang Lee, Sung-Ho Bae, Choong Seon Hong Abstract: Segment anything model (SAM) developed by Meta AI Research has recently attracted
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Original Paper: https://arxiv.org/abs/2306.13653 By: Jiaqi Ma, Tianheng Cheng, Guoli Wang, Qian Zhang, Xinggang Wang, Lefei Zhang Abstract: Image restoration aims to reconstruct degraded images, e.g., denoising or deblurring. Existing works focus on designing task-specific methods and there are inadequate attempts at universal methods. However,
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Original Paper: https://arxiv.org/abs/2302.05698 By: Jiacheng Ye, Zhiyong Wu, Jiangtao Feng, Tao Yu, Lingpeng Kong Abstract: Large pretrained language models (LMs) have shown impressive In-Context Learning (ICL) ability, where the model learns to do an unseen task via a prompt consisting of input-output examples as the
