research-papers
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
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
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
research-papers
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
research-papers
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
research-papers
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
research-papers
Original Paper: https://arxiv.org/abs/2408.04259 By: Ziyuan Zhuang, Zhiyang Zhang, Sitao Cheng, Fangkai Yang, Jia Liu, Shujian Huang, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, Qi Zhang Abstract Retrieval-augmented generation (RAG) methods encounter difficulties when addressing complex questions like multi-hop queries. While iterative retrieval methods improve performance by
research-papers
Original Paper: https://arxiv.org/abs/2407.18418 By: Bingbing Wen, Jihan Yao, Shangbin Feng, Chenjun Xu, Yulia Tsvetkov, Bill Howe, Lucy Lu Wang Abstract: Abstention, the refusal of large language models (LLMs) to provide an answer, is increasingly recognized for its potential to mitigate hallucinations and enhance safety in
research-papers
Original Paper: https://arxiv.org/abs/2408.04619 By: Aeree Cho, Grace C. Kim, Alexander Karpekov, Alec Helbling, Zijie J. Wang, Seongmin Lee, Benjamin Hoover, Duen Horng Chau Abstract: Transformers have revolutionized machine learning, yet their inner workings remain opaque to many. We present Transformer Explainer, an interactive visualization tool
research-papers
Original Paper: https://arxiv.org/abs/2408.04187 By: Junde Wu, Jiayuan Zhu, Yunli Qi Abstract: We introduce a novel graph-based Retrieval-Augmented Generation (RAG) framework specifically designed for the medical domain, called \textbf{MedGraphRAG}, aimed at enhancing Large Language Model (LLM) capabilities and generating evidence-based results, thereby improving safety and
research-papers
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
research-papers
Original Paper: https://arxiv.org/abs/2408.03314 By: Charlie Snell, Jaehoon Lee, Kelvin Xu, Aviral Kumar Abstract: Enabling LLMs to improve their outputs by using more test-time computation is a critical step towards building generally self-improving agents that can operate on open-ended natural language. In this paper, we study