research-papers
Original Paper: https://arxiv.org/abs/2407.19594 By: Tianhao Wu, Weizhe Yuan, Olga Golovneva, Jing Xu, Yuandong Tian, Jiantao Jiao, Jason Weston, Sainbayar Sukhbaatar Abstract: Large Language Models (LLMs) are rapidly surpassing human knowledge in many domains. While improving these models traditionally relies on costly human data, recent self-rewarding
research-papers
Original Paper: https://arxiv.org/abs/2407.18219 By: Yuxiao Qu, Tianjun Zhang, Naman Garg, Aviral Kumar Abstract: A central piece in enabling intelligent agentic behavior in foundation models is to make them capable of introspecting upon their behavior, and reasoning, and correcting their mistakes as more computation or interaction
research-papers
Original Paper: https://arxiv.org/abs/2407.19825 By: Sania Nayab, Giulio Rossolini, Giorgio Buttazzo, Nicolamaria Manes, Fabrizio Giacomelli Abstract: Today's large language models (LLMs) can solve challenging question-answering tasks, and prompt engineering techniques, such as chain-of-thought (CoT), have gained attention for enhancing the explanation and correctness of
research-papers
Original Paper: https://arxiv.org/abs/2407.18416 By: Vinay Samuel, Henry Peng Zou, Yue Zhou, Shreyas Chaudhari, Ashwin Kalyan, Tanmay Rajpurohit, Ameet Deshpande, Karthik Narasimhan, Vishvak Murahari Abstract: Persona agents, LLMs designed to act according to assigned personas, show impressive contextual responses across various sectors like education, healthcare, and
research-papers
Original Paper: https://arxiv.org/pdf/2407.20183 By: Zehui Chen, Kuikun Liu, Qiuchen Wang, Jiangning Liu, Wenwei Zhang, Kai Chen, Feng Zhao Abstract: Information seeking and integration is a complex cognitive task that consumes enormous time and effort. Inspired by the remarkable progress of Large Language Models, recent works
research-papers
Original Paper: https://arxiv.org/abs/2407.16833 By: Zhuowan Li, Cheng Li, Mingyang Zhang, Qiaozhu Mei, Michael Bendersky Abstract: Retrieval Augmented Generation (RAG) has been a powerful tool for Large Language Models (LLMs) to efficiently process overly lengthy contexts. However, recent LLMs like Gemini-1.5 and GPT-4 show exceptional
research-papers
Original Paper: https://arxiv.org/abs/2407.09252 By: David Rau, Shuai Wang, Hervé Déjean, Stéphane Clinchant Abstract Retrieval-Augmented Generation (RAG) allows for overcoming the limited knowledge of LLMs by extending the input with external information. As a consequence, the contextual inputs to the model become much longer which slows
research-papers
Original Paper: https://arxiv.org/abs/2407.16908 By: Georgios Kollias, Payel Das, Subhajit Chaudhury Abstract: Addressing the issue of hallucinations in large language models (LLMs) is a critical challenge. As the cognitive mechanisms of hallucination have been related to memory, here we explore hallucination for LLM that is enabled
research-papers
Original Paper: https://arxiv.org/abs/2407.14057 By: Qichen Fu, Minsik Cho, Thomas Merth, Sachin Mehta, Mohammad Rastegari, Mahyar Najibi Abstract The inference of transformer-based large language models consists of two sequential stages: 1) a prefilling stage to compute the KV cache of prompts and generate the first token
research-papers
Original Paper: https://arxiv.org/abs/2407.13647 By: Yuqing Yang, Yan Ma, Pengfei Liu Abstract: When large language models (LLMs) exceed human-level capabilities, it becomes increasingly challenging to provide full-scale and accurate supervisions for these models. Weak-to-strong learning, which leverages a less capable model to unlock the latent abilities
research-papers
Original Paper: https://arxiv.org/abs/2306.00978 By: Ji Lin, Jiaming Tang, Haotian Tang, Shang Yang, Wei-Ming Chen, Wei-Chen Wang, Guangxuan Xiao, Xingyu Dang, Chuang Gan, Song Han Abstract: Large language models (LLMs) have transformed numerous AI applications. On-device LLM is becoming increasingly important: running LLMs locally on edge
research-papers
Original Paper: https://arxiv.org/abs/2407.11963 By: Mo Li, Songyang Zhang, Yunxin Liu, Kai Chen Abstract: In evaluating the long-context capabilities of large language models (LLMs), identifying content relevant to a user's query from original long documents is a crucial prerequisite for any LLM to answer