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Original Paper: https://arxiv.org/abs/2407.13692 By: Jan Hendrik Kirchner, Yining Chen, Harri Edwards, Jan Leike, Nat McAleese, Yuri Burda Abstract: One way to increase confidence in the outputs of Large Language Models (LLMs) is to support them with reasoning that is clear and easy to check – a
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Original Paper: https://arxiv.org/abs/2407.21712 By: Xi Wang, Procheta Sen, Ruizhe Li, Emine Yilmaz Abstract: Despite the success of integrating large language models into the development of conversational systems, many studies have shown the effectiveness of retrieving and augmenting external knowledge for informative responses. Hence, many existing
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Original Paper: https://arxiv.org/abs/2407.20516 By: Zheyuan Liu, Guangyao Dou, Zhaoxuan Tan, Yijun Tian, Meng Jiang Abstract: Generative AI technologies have been deployed in many places, such as (multimodal) large language models and vision generative models. Their remarkable performance should be attributed to massive training data and
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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
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Original Paper: https://arxiv.org/abs/2407.21018 By: Yuhui Xu, Zhanming Jie, Hanze Dong, Lei Wang, Xudong Lu, Aojun Zhou, Amrita Saha, Caiming Xiong, Doyen Sahoo Abstract: Large Language Models (LLMs) have revolutionized the field of natural language processing, achieving unprecedented performance across a variety of applications by leveraging
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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
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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
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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
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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
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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
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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
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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
