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Original Paper: https://arxiv.org/abs/2310.00259 By: Zouying Cao, Yifei Yang, Hai Zhao Abstract: While LLMs have garnered widespread applications across various domains due to their powerful language understanding and generation capabilities, the detection of non-factual or hallucinatory content generated by LLMs remains scarce. Currently, one significant challenge
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Original Paper: https://arxiv.org/abs/2308.10379 By: Bilgehan Sel, Ahmad Al-Tawaha, Vanshaj Khattar, Ruoxi Jia, Ming Jin Abstract: Current literature, aiming to surpass the "Chain-of-Thought" approach, often resorts to an external modus operandi involving halting, modifying, and then resuming the generation process to boost Large Language
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Original Paper: https://arxiv.org/abs/2309.16797 By: Chrisantha Fernando, Dylan Banarse, Henryk Michalewski, Simon Osindero, Tim Rocktäschel Abstract: Popular prompt strategies like Chain-of-Thought Prompting can dramatically improve the reasoning abilities of Large Language Models (LLMs) in various domains. However, such hand-crafted prompt-strategies are often sub-optimal. In this paper,
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Original Paper: https://arxiv.org/abs/2309.11495 By: Shehzaad Dhuliawala, Mojtaba Komeili, Jing Xu, Roberta Raileanu, Xian Li, Asli Celikyilmaz, Jason Weston Abstract: Generation of plausible yet incorrect factual information, termed hallucination, is an unsolved issue in large language models. We study the ability of language models to deliberate
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Original Paper: https://arxiv.org/abs/2309.11064 By: Vipula Rawte, Prachi Priya, S.M Towhidul Islam Tonmoy, S M Mehedi Zaman, Amit Sheth, Amitava Das Abstract: As Large Language Models (LLMs) have advanced, they have brought forth new challenges, with one of the prominent issues being LLM hallucination. While
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Original Paper: https://arxiv.org/abs/2309.08532 By: Qingyan Guo, Rui Wang, Junliang Guo, Bei Li, Kaitao Song, Xu Tan, Guoqing Liu, Jiang Bian, Yujiu Yang Abstract: Large Language Models (LLMs) excel in various tasks, but they rely on carefully crafted prompts that often demand substantial human effort. To
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Original Paper: https://arxiv.org/abs/2309.06653 By: Wan-Jin Lu, Zhen-Yin Zhao, F. Y. Wang, Z. G. Dai Abstract: Fast radio bursts (FRBs) are energetic millisecond phenomena in radio band. Polarimetric studies of repeating FRBs indicate that many of these sources occupy extreme and complex magneto-ionized environments. Recently, a
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Original Paper: https://arxiv.org/abs/2309.04269 By: Griffin Adams, Alexander Fabbri, Faisal Ladhak, Eric Lehman, Noémie Elhadad Abstract: Selecting the ``right'' amount of information to include in a summary is a difficult task. A good summary should be detailed and entity-centric without being overly dense and
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Original Paper: https://arxiv.org/abs/2309.04379 By: Dongming Wu, Wencheng Han, Tiancai Wang, Yingfei Liu, Xiangyu Zhang, Jianbing Shen Abstract: A new trend in the computer vision community is to capture objects of interest following flexible human command represented by a natural language prompt. However, the progress of
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Original Paper: https://arxiv.org/abs/2306.00526 By: Wenjin Wang, Yunhao Li, Yixin Ou, Yin Zhang Abstract: Layout-aware pre-trained models has achieved significant progress on document image question answering. They introduce extra learnable modules into existing language models to capture layout information within document images from text bounding box
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Original Paper: https://arxiv.org/abs/2309.02848 By: Xuanwen Huang, Kaiqiao Han, Dezheng Bao, Quanjin Tao, Zhisheng Zhang, Yang Yang, Qi Zhu Abstract: Text-attributed Graphs (TAGs) are commonly found in the real world, such as social networks and citation networks, and consist of nodes represented by textual descriptions. Currently,
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Original Paper: https://arxiv.org/abs/2309.01219 By: Yue Zhang, Yafu Li, Leyang Cui, Deng Cai, Lemao Liu, Tingchen Fu, Xinting Huang, Enbo Zhao, Yu Zhang, Yulong Chen, Longyue Wang, Anh Tuan Luu, Wei Bi, Freda Shi, Shuming Shi Abstract: While large language models (LLMs) have demonstrated remarkable capabilities
