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Original Paper: https://arxiv.org/abs/2409.06857 By: Lihu Chen, Gaël Varoquaux Abstract: Large Language Models (LLMs) have made significant progress in advancing artificial general intelligence (AGI), leading to the development of increasingly large models such as GPT-4 and LLaMA-405B. However, scaling up model sizes results in exponentially higher
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Original Paper: https://arxiv.org/abs/2409.07429 By: Zora Zhiruo Wang, Jiayuan Mao, Daniel Fried, Graham Neubig Abstract: Despite the potential of language model-based agents to solve real-world tasks such as web navigation, current methods still struggle with long-horizon tasks with complex action trajectories. In contrast, humans can flexibly
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Published Date: 7 Sep 2024 Original Paper: https://arxiv.org/abs/2409.04833 By: Zhyar Rzgar K Rostam, Sándor Szénási, Gábor Kertész Abstract: In recent years, large language models (LLMs) have achieved remarkable success in natural language processing (NLP). LLMs require an extreme amount of parameters to attain high performance.
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Original Paper: https://arxiv.org/abs/2409.03271v1 By: Yu Wang, Shiwan Zhao, Zhihu Wang, Heyuan Huang, Ming Fan, Yubo Zhang, Zhixing Wang, Haijun Wang, Ting Liu Abstract: The Chain-of-Thought (CoT) paradigm has emerged as a critical approach for enhancing the reasoning capabilities of large language models (LLMs). However, despite
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Original Paper: https://arxiv.org/abs/2409.02977 By: Junwei Liu, Kaixin Wang, Yixuan Chen, Xin Peng, Zhenpeng Chen, Lingming Zhang, Yiling Lou Abstract: The recent advance in Large Language Models (LLMs) has shaped a new paradigm of AI agents, i.e., LLM-based agents. Compared to standalone LLMs, LLM-based agents
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Original Paper: https://arxiv.org/abs/2408.16984 By: Tan Zhi-Xuan, Micah Carroll, Matija Franklin, Hal Ashton Abstract: The dominant practice of AI alignment assumes (1) that preferences are an adequate representation of human values (2) that human rationality can be understood in terms of maximizing the satisfaction of preferences
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Original Paper: https://arxiv.org/abs/2408.01262 By: Kunlun Zhu, Yifan Luo, Dingling Xu, Ruobing Wang, Shi Yu, Shuo Wang, Yukun Yan, Zhenghao Liu, Xu Han, Zhiyuan Liu, Maosong Sun Abstract: Retrieval-Augmented Generation (RAG) systems have demonstrated their advantages in alleviating the hallucination of Large Language Models (LLMs). Existing
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Original Paper: https://arxiv.org/pdf/2408.11796 By: Sharath Turuvekere Sreenivas, Saurav Muralidharan, Raviraj Joshi, Marcin Chochowski, Mostofa Patwary, Mohammad Shoeybi, Bryan Catanzaro, Jan Kautz, Pavlo Molchanov Abstract We present a comprehensive report on compressing the Llama 3.1 8B and Mistral NeMo 12B models to 4B and 8B
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Original Paper: https://arxiv.org/abs/2408.12599 By: Xun Liang, Hanyu Wang, Yezhaohui Wang, Shichao Song, Jiawei Yang, Simin Niu, Jie Hu, Dan Liu, Shunyu Yao, Feiyu Xiong, Zhiyu Li Abstract: In Natural Language Processing (NLP), Large Language Models (LLMs) have demonstrated high text generation quality. However, in real-world
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Original Paper: https://arxiv.org/pdf/2402.03367 Code Sample: https://github.com/NirDiamant/RAG_Techniques/blob/main/all_rag_techniques/fusion_retrieval.ipynb RAG-Fusion or Fusion-retrieval RAG, is an advanced technique that enhances the traditional Retrieval Augmented Generation (RAG) approach used in AI and natural language processing. This method
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Original Papers: * Query Rewriting: https://arxiv.org/pdf/2305.14283 * Step-back Prompting: https://arxiv.org/abs/2310.06117 * Sub-query Decomposition: https://arxiv.org/pdf/2404.00610 Code Sample: https://github.com/NirDiamant/RAG_Techniques/blob/main/all_rag_techniques/query_transformations.ipynb Query transformations are advanced techniques used to enhance
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Code Sample: https://github.com/NirDiamant/RAG_Techniques/blob/main/all_rag_techniques/reranking.ipynb Reranking is a powerful technique used in Retrieval-Augmented Generation (RAG) systems to refine and improve the relevance of retrieved documents. Here's a detailed explanation of reranking methods in RAG systems, along with their
