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Original Paper: https://arxiv.org/abs/2401.16818 By: Philipp Singer, Pascal Pfeiffer, Yauhen Babakhin, Maximilian Jeblick, Nischay Dhankhar, Gabor Fodor, Sri Satish Ambati Abstract: We present H2O-Danube, a series of small 1.8B language models consisting of H2O-Danube-1.8B, trained on 1T tokens, and the incremental improved H2O-Danube2-1.8B
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Original Paper: https://arxiv.org/abs/2307.15043 By: Andy Zou, Zifan Wang, Nicholas Carlini, Milad Nasr, J. Zico Kolter, Matt Fredrikson Abstract: Because "out-of-the-box" large language models are capable of generating a great deal of objectionable content, recent work has focused on aligning these models in an
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Original Paper: https://arxiv.org/html/2404.09170v1 By: Xiaoshu Chen, Sihang Zhou, Liang Ke, XinwangLiu Abstract Chain of thought finetuning aims to endow small student models with reasoning capacity to improve their performance towards a specific task by allowing them to imitate the reasoning procedure of large language models
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Original Paper: https://arxiv.org/html/2305.18290v2 By: Rafael Rafailov, Archit Sharma, Eric Mitchell, Stefano Ermon, Christopher D. Manning, Chelsea Finn Abstract: While large-scale unsupervised language models (LMs) learn broad world knowledge and some reasoning skills, achieving precise control of their behavior is difficult due to the completely unsupervised
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Original Paper: https://arxiv.org/abs/2404.07879 By: Vigneshwaran Shankaran, Rajesh Sharma Abstract: Online social media has become increasingly popular in recent years due to its ease of access and ability to connect with others. One of social media's main draws is its anonymity, allowing users to
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Original Paper: https://arxiv.org/abs/2404.05449 By: Wenyang Hui, Chengyue Jiang, Yan Wang, Kewei Tu Abstract: Large language models (LLMs) have demonstrated impressive capability in reasoning and planning when integrated with tree-search-based prompting methods. However, since these methods ignore the previous search experiences, they often make the same
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Original Paper: https://arxiv.org/html/2403.17419v1 Author: Deepak P., Queen’s University Belfast, UK (deepaksp@acm.org) Artificial Intelligence (AI) is evolving rapidly, bringing the topic of AI safety to the forefront of discussions. While ensuring AI systems are safe and dependable is crucial, there's a
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Original Paper: https://arxiv.org/abs/2404.04735 By: Bin Lei Abstract: Recent advancements in large language models, such as GPT-4, have demonstrated remarkable capabilities in processing standard queries. Despite these advancements, their performance substantially declines in \textbf{advanced mathematical problems requiring complex, multi-step logical reasoning}. To enhance their inferential
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Original Paper: https://arxiv.org/abs/2404.05225 By: Chuwei Luo, Yufan Shen, Zhaoqing Zhu, Qi Zheng, Zhi Yu, Cong Yao Abstract: Recently, leveraging large language models (LLMs) or multimodal large language models (MLLMs) for document understanding has been proven very promising. However, previous works that employ LLMs/MLLMs for
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Original Paper: https://www.anthropic.com/research/many-shot-jailbreaking Breaking Down the Jailbreak: The Trouble with AI's Big Brain In the world of AI, Large Language Models (LLMs) are like the brainiacs of the class, getting smarter and more talkative by the day. But, just like in any sci-fi
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Original Paper: https://arxiv.org/pdf/2403.04786.pdf Keeping Large Language Models Safe: What You Need to Know In the world of computers that understand and generate human-like text, big models called Large Language Models (LLMs) are a big deal. But, just like anything valuable, they're also
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Original Paper: https://arxiv.org/abs/2401.14295 By: Maciej Besta, Florim Memedi, Zhenyu Zhang, Robert Gerstenberger, Guangyuan Piao, Nils Blach, Piotr Nyczyk, Marcin Copik, Grzegorz Kwaśniewski, Jürgen Müller, Lukas Gianinazzi, Ales Kubicek, Hubert Niewiadomski, Aidan O'Mahony, Onur Mutlu, Torsten Hoefler Abstract: The field of natural language processing
