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Original Paper: https://arxiv.org/abs/2406.06326 By: Xiaoying Zhang, Baolin Peng, Ye Tian, Jingyan Zhou, Yipeng Zhang, Haitao Mi, Helen Meng Abstract: Large language models (LLMs) often struggle to provide up-to-date information due to their one-time training and the constantly evolving nature of the world. To keep LLMs
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Original Paper: https://arxiv.org/abs/2406.15126 By: Lin Long, Rui Wang, Ruixuan Xiao, Junbo Zhao, Xiao Ding, Gang Chen, Haobo Wang Abstract: Within the evolving landscape of deep learning, the dilemma of data quantity and quality has been a long-standing problem. The recent advent of Large Language Models
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Original Paper: https://arxiv.org/abs/2406.10209 By: Abhimanyu Hans, Yuxin Wen, Neel Jain, John Kirchenbauer, Hamid Kazemi, Prajwal Singhania, Siddharth Singh, Gowthami Somepalli, Jonas Geiping, Abhinav Bhatele, Tom Goldstein Abstract: Large language models can memorize and repeat their training data, causing privacy and copyright risks. To mitigate memorization,
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Original Paper: https://arxiv.org/abs/2406.08414 By: Chris Lu, Samuel Holt, Claudio Fanconi, Alex J. Chan, Jakob Foerster, Mihaela van der Schaar, Robert Tjarko Lange Abstract: Offline preference optimization is a key method for enhancing and controlling the quality of Large Language Model (LLM) outputs. Typically, preference optimization
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Original Paper: https://arxiv.org/abs/2401.06118v3 By: Vage Egiazarian, Andrei Panferov, Denis Kuznedelev, Elias Frantar, Artem Babenko, Dan Alistarh Abstract: The emergence of accurate open large language models (LLMs) has led to a race towards performant quantization techniques which can enable their execution on end-user devices. In this
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Original Paper: https://arxiv.org/abs/2406.04784 By: Ruihan Yang, Jiangjie Chen, Yikai Zhang, Siyu Yuan, Aili Chen, Kyle Richardson, Yanghua Xiao, Deqing Yang Abstract: Language agents powered by large language models (LLMs) are increasingly valuable as decision-making tools in domains such as gaming and programming. However, these agents
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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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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