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Original Paper: https://arxiv.org/abs/2408.09416 By: Hongyin Zhu Abstract: This paper carefully summarizes extensive and profound questions from all walks of life, focusing on the current high-profile AI field, covering multiple dimensions such as industry trends, academic research, technological innovation and business applications. This paper meticulously curates
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Original Paper: https://arxiv.org/abs/2408.10615 By: Ming Jiang, Tingting Huang, Biao Guo, Yao Lu, Feng Zhang Abstract: In recent years, Large language models (LLMs) have garnered significant attention due to their superior performance in complex reasoning tasks. However, recent studies may diminish their reasoning capabilities markedly when
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Original Paper: https://arxiv.org/abs/2408.08067 By: Dongyu Ru, Lin Qiu, Xiangkun Hu, Tianhang Zhang, Peng Shi, Shuaichen Chang, Cheng Jiayang, Cunxiang Wang, Shichao Sun, Huanyu Li, Zizhao Zhang, Binjie Wang, Jiarong Jiang, Tong He, Zhiguo Wang, Pengfei Liu, Yue Zhang, Zheng Zhang Abstract Despite Retrieval-Augmented Generation (RAG)
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Original Paper: https://arxiv.org/abs/2408.02479 By: Haolin Jin, Linghan Huang, Haipeng Cai, Jun Yan, Bo Li, Huaming Chen Abstract: One of the grand challenges of artificial general intelligence is developing agents capable of conducting scientific research and discovering new knowledge. While frontier models have already been used
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Original Paper: https://arxiv.org/abs/2408.08921 By: Boci Peng, Yun Zhu, Yongchao Liu, Xiaohe Bo, Haizhou Shi, Chuntao Hong, Yan Zhang, Siliang Tang Abstract Recently, Retrieval-Augmented Generation (RAG) has achieved remarkable success in addressing the challenges of Large Language Models (LLMs) without necessitating retraining. By referencing an external
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Original Paper: https://arxiv.org/abs/2408.08435 By: Shengran Hu, Cong Lu, Jeff Clune Abstract: Researchers are investing substantial effort in developing powerful general-purpose agents, wherein Foundation Models are used as modules within agentic systems (e.g. Chain-of-Thought, Self-Reflection, Toolformer). However, the history of machine learning teaches us that
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Original Paper: https://arxiv.org/pdf/2408.08435 By: Shengran Hu, Cong Lu, Jeff Clune Abstract Researchers are investing substantial effort in developing powerful general-purpose agents, wherein Foundation Models are used as modules within agentic systems (e.g. Chain-of-Thought, Self-Reflection, Toolformer). However, the history of machine learning teaches us that
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Original Paper: https://arxiv.org/abs/2408.07055 By: Yushi Bai, Jiajie Zhang, Xin Lv, Linzhi Zheng, Siqi Zhu, Lei Hou, Yuxiao Dong, Jie Tang, Juanzi Li Abstract: Current long context large language models (LLMs) can process inputs up to 100,000 tokens, yet struggle to generate outputs exceeding even
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Original Paper: https://arxiv.org/abs/2407.15186 By: Liang Shi, Zhengju Tang, Nan Zhang, Xiaotong Zhang, Zhi Yang Abstract: The increasing volume of data stored in relational databases has led to the need for efficient querying and utilization of this data in various sectors. However, writing SQL queries requires
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Original Paper: https://arxiv.org/abs/2408.04948 By: Bhaskarjit Sarmah, Benika Hall, Rohan Rao, Sunil Patel, Stefano Pasquali, Dhagash Mehta Abstract Extraction and interpretation of intricate information from unstructured text data arising in financial applications, such as earnings call transcripts, present substantial challenges to large language models (LLMs) even
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Original Paper: https://arxiv.org/abs/2408.02666 By: Tianlu Wang, Ilia Kulikov, Olga Golovneva, Ping Yu, Weizhe Yuan, Jane Dwivedi-Yu, Richard Yuanzhe Pang, Maryam Fazel-Zarandi, Jason Weston, Xian Li Abstract: Model-based evaluation is at the heart of successful model development -- as a reward model for training, and as
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Original Paper: https://arxiv.org/abs/2408.04560 By: Liat Ein-Dor, Orith Toledo-Ronen, Artem Spector, Shai Gretz, Lena Dankin, Alon Halfon, Yoav Katz, Noam Slonim Abstract: Prompts are how humans communicate with LLMs. Informative prompts are essential for guiding LLMs to produce the desired output. However, prompt engineering is often
