Athina AI Agent

research-papers

RAG-Fusion (Fusion Retrieval RAG)

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

research-papers

Query Transformations: Rewriting, Step-back Prompting, and Sub-query Decomposition

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

research-papers

Challenges and Responses in the Practice of Large Language Models

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

research-papers

Enhancing Robustness in Large Language Models: Prompting for Mitigating the Impact of Irrelevant Information

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

research-papers

RAGEval: Scenario Specific RAG Evaluation Dataset Generation Framework

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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RAGChecker: A Fine-grained Framework for Diagnosing Retrieval-Augmented Generation

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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The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery

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

research-papers

Graph Retrieval-Augmented Generation: A Survey

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

research-papers

Automated Design of Agentic Systems

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

research-papers

Automated Design of Agentic System

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

research-papers

LongWriter: Unleashing 10,000+ Word Generation from Long Context LLMs

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

research-papers

A Survey on Employing Large Language Models for Text-to-SQL Tasks

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