research-papers
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
research-papers
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
research-papers
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
research-papers
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
research-papers
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
research-papers
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
research-papers
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
research-papers
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
research-papers
Original Paper: https://cdn.openai.com/llm-critics-help-catch-llm-bugs-paper.pdf By: OpenAI Abstract: Reinforcement learning from human feedback (RLHF) is fundamentally limited by the capacity of humans to correctly evaluate model output. To improve human evaluation ability and overcome that limitation this work trains “critic” models that help humans to more accurately
research-papers
Original Paper: https://arxiv.org/abs/2406.19292 By: Zheyang Xiong, Vasilis Papageorgiou, Kangwook Lee, Dimitris Papailiopoulos Abstract: Recent studies have shown that Large Language Models (LLMs) struggle to accurately retrieve information and maintain reasoning capabilities when processing long-context inputs. To address these limitations, we propose a finetuning approach utilizing
research-papers
Original Paper: https://arxiv.org/abs/2406.17744 By: Weizhe Yuan, Ilia Kulikov, Ping Yu, Kyunghyun Cho, Sainbayar Sukhbaatar, Jason Weston, Jing Xu Abstract: Aligned instruction following models can better fulfill user requests than their unaligned counterparts. However, it has been shown that there is a length bias in evaluation
research-papers
Original Paper: https://arxiv.org/abs/2406.11409 By: CodeGemma Team: Heri Zhao, Jeffrey Hui, Joshua Howland, Nam Nguyen, Siqi Zuo, Andrea Hu, Christopher A. Choquette-Choo, Jingyue Shen, Joe Kelley, Kshitij Bansal, Luke Vilnis, Mateo Wirth, Paul Michel, Peter Choy, Pratik Joshi, Ravin Kumar, Sarmad Hashmi, Shubham Agrawal, Zhitao Gong,