research-papers
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
research-papers
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
research-papers
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
research-papers
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
research-papers
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
research-papers
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
research-papers
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
research-papers
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
research-papers
Original Paper: https://arxiv.org/abs/2404.03592 By: Zhengxuan Wu, Aryaman Arora, Zheng Wang, Atticus Geiger, Dan Jurafsky, Christopher D. Manning, Christopher Potts Abstract: Parameter-efficient finetuning (PEFT) methods seek to adapt large neural models via updates to a small number of weights. However, much prior interpretability work has shown
research-papers
Original Paper: https://arxiv.org/pdf/2310.06825.pdf By: Albert Q. Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lucile Saulnier, Lélio Renard Lavaud, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix,
research-papers
Original Paper: https://arxiv.org/abs/2310.04948 By: Defu Cao, Furong Jia, Sercan O Arik, Tomas Pfister, Yixiang Zheng, Wen Ye, Yan Liu Abstract: The past decade has witnessed significant advances in time series modeling with deep learning. While achieving state-of-the-art results, the best-performing architectures vary highly across applications
research-papers
Original Paper: https://arxiv.org/abs/2311.01449 By: Chau Minh Pham, Alexander Hoyle, Simeng Sun, Philip Resnik, Mohit Iyyer Abstract: Topic modeling is a well-established technique for exploring text corpora. Conventional topic models (e.g., LDA) represent topics as bags of words that often require "reading the tea