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Original Paper: https://arxiv.org/abs/2402.07927 By: Pranab Sahoo, Ayush Kumar Singh, Sriparna Saha, Vinija Jain, Samrat Mondal, Aman Chadha Abstract: Prompt engineering has emerged as an indispensable technique for extending the capabilities of large language models (LLMs) and vision-language models (VLMs). This approach leverages task-specific instructions, known
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Original Paper: https://arxiv.org/abs/2402.02420 By: Yuxia Wang, Minghan Wang, Muhammad Arslan Manzoor, Fei Liu, Georgi Georgiev, Rocktim Jyoti Das, Preslav Nakov Abstract: Large language models (LLMs), especially when instruction-tuned for chat, have become part of our daily lives, freeing people from the process of searching, extracting,
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Original Paper: https://arxiv.org/abs/2401.16380 By: Pratyush Maini, Skyler Seto, He Bai, David Grangier, Yizhe Zhang, Navdeep Jaitly Abstract: Large language models are trained on massive scrapes of the web, which are often unstructured, noisy, and poorly phrased. Current scaling laws show that learning from such data
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Original Paper: https://arxiv.org/abs/2401.16541 By: Sabur Butt, Fazlourrahman Balouchzahi, Abdul Gafar Manuel Meque, Maaz Amjad, Hector G. Ceballos Cancino, Grigori Sidorov, Alexander Gelbukh Abstract: The intricate relationship between human decision-making and emotions, particularly guilt and regret, has significant implications on behavior and well-being. Yet, these emotions
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Original Paper: https://arxiv.org/abs/2401.14447 By: Zijie J. Wang, Aishwarya Chakravarthy, David Munechika, Duen Horng Chau Abstract: Large language models (LLMs) require well-crafted prompts for effective use. Prompt engineering, the process of designing prompts, is challenging, particularly for non-experts who are less familiar with AI technologies. While
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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
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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
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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
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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,
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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
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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
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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