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Original Paper: https://arxiv.org/abs/2406.04692 By: Junlin Wang, Jue Wang, Ben Athiwaratkun, Ce Zhang, James Zou Abstract: Recent advances in large language models (LLMs) demonstrate substantial capabilities in natural language understanding and generation tasks. With the growing number of LLMs, how to harness the collective expertise of
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Original Paper: https://arxiv.org/abs/2405.02664 By: Roomani Srivastava, Suraj Prasad, Lipika Bhat, Sarvesh Deshpande, Barnali Das, Kshitij Jadhav Abstract: A major roadblock in the seamless digitization of medical records remains the lack of interoperability of existing records. Extracting relevant medical information required for further treatment planning or
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Original Paper: https://arxiv.org/abs/2403.10131 By: Tianjun Zhang, Shishir G. Patil, Naman Jain, Sheng Shen, Matei Zaharia, Ion Stoica, Joseph E. Gonzalez Abstract: Pretraining Large Language Models (LLMs) on large corpora of textual data is now a standard paradigm. When using these LLMs for many downstream applications,
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In recent years, LLMs have become instrumental across various industries, revolutionizing fields such as customer service, healthcare, and legal document processing. However, a significant challenge lies in their ability to adapt to highly specialized tasks, where domain-specific knowledge is essential for accuracy. This is where Retrieval Augmented Fine-Tuning (RAFT) steps
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Retrieval-Augmented Generation (RAG) is a powerful technique that enhances large language models (LLMs) by combining their generative capabilities with the ability to retrieve relevant external information. Outline 1. Introduction * Importance and benefits of RAG 2. Understanding Retrieval-Augmented Generation * What is RAG? * Key differences from traditional LLMs 3. Steps to Integrate
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Original Paper: https://arxiv.org/abs/2405.18137 By: Kazuki Egashira, Mark Vero, Robin Staab, Jingxuan He, Martin Vechev Abstract: Quantization leverages lower-precision weights to reduce the memory usage of large language models (LLMs) and is a key technique for enabling their deployment on commodity hardware. While LLM quantization'
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Original Paper: https://arxiv.org/abs/2311.11538 By: Jiahao Yu, Yuhang Wu, Dong Shu, Mingyu Jin, Sabrina Yang, Xinyu Xing Abstract: In the rapidly evolving landscape of artificial intelligence, ChatGPT has been widely used in various applications. The new feature - customization of ChatGPT models by users to cater
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Original Paper: https://arxiv.org/abs/2402.04291 By: Wei Huang, Yangdong Liu, Haotong Qin, Ying Li, Shiming Zhang, Xianglong Liu, Michele Magno, Xiaojuan Qi Abstract: Pretrained large language models (LLMs) exhibit exceptional general language processing capabilities but come with significant demands on memory and computational resources. As a powerful
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Original Paper: https://arxiv.org/abs/2305.09993 By: Weijia Xu, Andrzej Banburski-Fahey, Nebojsa Jojic Abstract: We introduce Reprompting, an iterative sampling algorithm that automatically learns the Chain-of-Thought (CoT) recipes for a given task without human intervention. Through Gibbs sampling, Reprompting infers the CoT recipes that work consistently well for
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Original Paper: https://arxiv.org/abs/2404.14219 By: Marah Abdin, Sam Ade Jacobs, Ammar Ahmad Awan, Jyoti Aneja, Ahmed Awadallah, Hany Awadalla, Nguyen Bach, Amit Bahree, Arash Bakhtiari, Jianmin Bao, Harkirat Behl, Alon Benhaim, Misha Bilenko, Johan Bjorck, Sébastien Bubeck, Qin Cai, Martin Cai, Caio César Teodoro Mendes, Weizhu
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Original Paper: https://arxiv.org/abs/2402.18510 By: Kaiyue Wen, Xingyu Dang, Kaifeng Lyu Abstract: This paper investigates the gap in representation powers of Recurrent Neural Networks (RNNs) and Transformers in the context of solving algorithmic problems. We focus on understanding whether RNNs, known for their memory efficiency in
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Original Paper: https://arxiv.org/abs/2401.00595 By: Moran Mizrahi, Guy Kaplan, Dan Malkin, Rotem Dror, Dafna Shahaf, Gabriel Stanovsky Abstract: Recent advances in large language models (LLMs) have led to the development of various evaluation benchmarks. These benchmarks typically rely on a single instruction template for evaluating all
