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Original Paper: https://arxiv.org/abs/2407.14057 By: Qichen Fu, Minsik Cho, Thomas Merth, Sachin Mehta, Mohammad Rastegari, Mahyar Najibi Abstract The inference of transformer-based large language models consists of two sequential stages: 1) a prefilling stage to compute the KV cache of prompts and generate the first token
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Original Paper: https://arxiv.org/abs/2407.13647 By: Yuqing Yang, Yan Ma, Pengfei Liu Abstract: When large language models (LLMs) exceed human-level capabilities, it becomes increasingly challenging to provide full-scale and accurate supervisions for these models. Weak-to-strong learning, which leverages a less capable model to unlock the latent abilities
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Original Paper: https://arxiv.org/abs/2306.00978 By: Ji Lin, Jiaming Tang, Haotian Tang, Shang Yang, Wei-Ming Chen, Wei-Chen Wang, Guangxuan Xiao, Xingyu Dang, Chuang Gan, Song Han Abstract: Large language models (LLMs) have transformed numerous AI applications. On-device LLM is becoming increasingly important: running LLMs locally on edge
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Original Paper: https://arxiv.org/abs/2407.11963 By: Mo Li, Songyang Zhang, Yunxin Liu, Kai Chen Abstract: In evaluating the long-context capabilities of large language models (LLMs), identifying content relevant to a user's query from original long documents is a crucial prerequisite for any LLM to answer
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Original Paper: https://arxiv.org/abs/2407.12216 By: Garima Agrawal, Tharindu Kumarage, Zeyad Alghamdi, Huan Liu Abstract: Large Language Models (LLMs) are proficient at generating coherent and contextually relevant text but face challenges when addressing knowledge-intensive queries in domain-specific and factual question-answering tasks. Retrieval-augmented generation (RAG) systems mitigate this
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Original Paper: https://arxiv.org/abs/2407.09025 By: Yuzhang Tian, Jianbo Zhao, Haoyu Dong, Junyu Xiong, Shiyu Xia, Mengyu Zhou, Yun Lin, José Cambronero, Yeye He, Shi Han, Dongmei Zhang Abstract: Spreadsheets, with their extensive two-dimensional grids, various layouts, and diverse formatting options, present notable challenges for large language
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Original Paper: https://arxiv.org/abs/2407.08223 By: Zilong Wang, Zifeng Wang, Long Le, Huaixiu Steven Zheng, Swaroop Mishra, Vincent Perot, Yuwei Zhang, Anush Mattapalli, Ankur Taly, Jingbo Shang, Chen-Yu Lee, Tomas Pfister Abstract: Retrieval augmented generation (RAG) combines the generative abilities of large language models (LLMs) with external
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Original Paper: https://arxiv.org/abs/2407.06023v1 By: Ping Yu, Jing Xu, Jason Weston, Ilia Kulikov Abstract: Large language models (LLMs) can spend extra compute during inference to generate intermediate thoughts, which helps to produce better final responses. Since Chain-of-Thought (Wei et al., 2022), many such System 2 techniques
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Original Paper: https://jykoh.com/search-agents By: Jing Yu Koh, Stephen McAleer, Daniel Fried, Ruslan Salakhutdinov Abstract: Autonomous agents powered by language models (LMs) have demonstrated promise in their ability to perform decision-making tasks such as web automation. However, a fundamental challenge remains: LMs, primarily optimized for natural language understanding
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Original Paper: https://arxiv.org/abs/2406.18665v2 By: Isaac Ong, Amjad Almahairi, Vincent Wu, Wei-Lin Chiang, Tianhao Wu, Joseph E. Gonzalez, M Waleed Kadous, Ion Stoica Abstract: Large language models (LLMs) exhibit impressive capabilities across a wide range of tasks, yet the choice of which model to use often
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Original Paper: https://arxiv.org/abs/2407.01370 By: Philippe Laban, Alexander R. Fabbri, Caiming Xiong, Chien-Sheng Wu Abstract: LLMs and RAG systems are now capable of handling millions of input tokens or more. However, evaluating the output quality of such systems on long-context tasks remains challenging, as tasks like
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Original Paper: https://arxiv.org/abs/2407.12036 By: Giorgio Roffo Abstract: This tutorial explores the advancements and challenges in the development of Large Language Models (LLMs) such as ChatGPT and Gemini. It addresses inherent limitations like temporal knowledge cutoffs, mathematical inaccuracies, and the generation of incorrect information, proposing solutions
