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Original Paper: https://arxiv.org/abs/2312.17673 By: Julien Piet, Maha Alrashed, Chawin Sitawarin, Sizhe Chen, Zeming Wei, Elizabeth Sun, Basel Alomair, David Wagner Abstract: Large Language Models (LLMs) are attracting significant research attention due to their instruction-following abilities, allowing users and developers to leverage LLMs for a variety
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Original Paper: https://arxiv.org/abs/2310.01775 By: Yewon Lee, Philip Huang, Krishna Murthy Jatavallabhula, Andrew Z. Li, Fabian Damken, Eric Heiden, Kevin Smith, Derek Nowrouzezahrai, Fabio Ramos, Florian Shkurti Abstract: Planning for many manipulation tasks, such as using tools or assembling parts, often requires both symbolic and geometric
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Original Paper: https://arxiv.org/abs/2401.02034 By: Wei Zhu, Wenfeng Li, Xing Tian, Pengfei Wang, Xiaoling Wang, Jin Chen, Yuanbin Wu, Yuan Ni, Guotong Xie Abstract: Knowledge of the medical decision process, which can be modeled as medical decision trees (MDTs), is critical to build clinical decision support
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Original Paper: https://arxiv.org/abs/2401.00812 By: Ke Yang, Jiateng Liu, John Wu, Chaoqi Yang, Yi R. Fung, Sha Li, Zixuan Huang, Xu Cao, Xingyao Wang, Yiquan Wang, Heng Ji, Chengxiang Zhai Abstract: The prominent large language models (LLMs) of today differ from past language models not only
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Original Paper: https://arxiv.org/abs/2312.16171v1 By: Sondos Mahmoud Bsharat, Aidar Myrzakhan, Zhiqiang Shen Abstract: This paper introduces 26 guiding principles designed to streamline the process of querying and prompting large language models. Our goal is to simplify the underlying concepts of formulating questions for various scales of
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Original Paper: https://arxiv.org/abs/2311.11261 By: Jiaming Zhang, Xingjun Ma, Xin Wang, Lingyu Qiu, Jiaqi Wang, Yu-Gang Jiang, Jitao Sang Abstract: With the rapid advancement of multimodal learning, pre-trained Vision-Language Models (VLMs) such as CLIP have demonstrated remarkable capacities in bridging the gap between visual and language
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Original Paper: https://arxiv.org/abs/2312.13115 By: Youjia Li, Jianjun Shi, Zheng Zhang Abstract: Code generation stands as a powerful technique in modern software development, improving development efficiency, reducing errors, and fostering standardization and consistency. Recently, ChatGPT has exhibited immense potential in automatic code generation. However, existing researches
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Original Paper: https://arxiv.org/abs/2312.12037 By: Sichao Xiong, Yigit Ihlamur Abstract: This research introduces an innovative evaluation method for the "founder-idea" fit in early-stage startups, utilizing advanced large language model techniques to assess founders' profiles against their startup ideas to enhance decision-making. Embeddings, self-play,
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Original Paper: https://arxiv.org/abs/2312.12416 By: Shweta Mahajan, Tanzila Rahman, Kwang Moo Yi, Leonid Sigal Abstract: The quality of the prompts provided to text-to-image diffusion models determines how faithful the generated content is to the user's intent, often requiring `prompt engineering'. To harness visual
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Original Paper: https://arxiv.org/abs/2310.14845 By: Mouxiang Chen, Zemin Liu, Chenghao Liu, Jundong Li, Qiheng Mao, Jianling Sun Abstract: Recent research has demonstrated the efficacy of pre-training graph neural networks (GNNs) to capture the transferable graph semantics and enhance the performance of various downstream tasks. However, the
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Original Paper: https://arxiv.org/abs/2303.04998 By: Ning Liao, Bowen Shi, Xiaopeng Zhang, Min Cao, Junchi Yan, Qi Tian Abstract: Prompt learning has achieved great success in efficiently exploiting large-scale pre-trained models in natural language processing (NLP). It reformulates the downstream tasks as the generative pre-training ones to
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Original Paper: https://arxiv.org/abs/2305.18396 By: Xuanqi Liu, Zhuotao Liu Abstract: The community explored to build private inference frameworks for transformer-based large language models (LLMs) in a server-client setting, where the server holds the model parameters and the client inputs its private data (or prompt) for inference.