research-papers
Original Paper: https://arxiv.org/abs/2309.17249 By: Han Zhou, Xingchen Wan, Lev Proleev, Diana Mincu, Jilin Chen, Katherine Heller, Subhrajit Roy Abstract: Prompting and in-context learning (ICL) have become efficient learning paradigms for large language models (LLMs). However, LLMs suffer from prompt brittleness and various bias factors in
research-papers
Original Paper: https://arxiv.org/abs/2401.14423 By: Xavier Amatriain Abstract: Prompt design and engineering has rapidly become essential for maximizing the potential of large language models. In this paper, we introduce core concepts, advanced techniques like Chain-of-Thought and Reflection, and the principles behind building LLM-based agents. Finally, we
research-papers
Original Paper: https://arxiv.org/abs/2401.11120 By: David Oniani, Xizhi Wu, Shyam Visweswaran, Sumit Kapoor, Shravan Kooragayalu, Katelyn Polanska, Yanshan Wang Abstract: Background Large Language Models (LLMs), enhanced with Clinical Practice Guidelines (CPGs), can significantly improve Clinical Decision Support (CDS). However, methods for incorporating CPGs into LLMs are
research-papers
Original Paper: https://arxiv.org/abs/2401.10491 By: Fanqi Wan, Xinting Huang, Deng Cai, Xiaojun Quan, Wei Bi, Shuming Shi Abstract While training large language models (LLMs) from scratch can generate models with distinct functionalities and strengths, it comes at significant costs and may result in redundant capabilities. Alternatively,
research-papers
Original Paper: https://arxiv.org/abs/2401.10061 By: Jie Qin, Jie Wu, Weifeng Chen, Yuxi Ren, Huixia Li, Hefeng Wu, Xuefeng Xiao, Rui Wang, Shilei Wen Abstract: Diffusion models have opened up new avenues for the field of image generation, resulting in the proliferation of high-quality models shared on
research-papers
Original Paper: https://arxiv.org/abs/2401.05787 By: Md Rizwan Parvez Abstract: While chain-of-thought (CoT) prompting has revolutionized how LLMs perform reasoning tasks, its current methods and variations (e.g, Self-consistency, ReACT, Reflexion, Tree-of-Thoughts (ToT), Cumulative Reasoning (CR)) suffer from limitations like slowness, limited context grounding, hallucination and inconsistent
research-papers
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
research-papers
Original Paper: https://arxiv.org/abs/2401.04088 By: Albert Q. Jiang, Alexandre Sablayrolles, Antoine Roux, Arthur Mensch, Blanche Savary, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Emma Bou Hanna, Florian Bressand, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Sandeep
research-papers
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
research-papers
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
research-papers
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
research-papers
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
