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Original Paper: https://arxiv.org/abs/2312.04344 By: Diego Mollá Abstract: This paper reports on the use of prompt engineering and GPT-3.5 for biomedical query-focused multi-document summarisation. Using GPT-3.5 and appropriate prompts, our system achieves top ROUGE-F1 results in the task of obtaining short-paragraph-sized answers to biomedical
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Original Paper: https://arxiv.org/abs/2311.05232 By: Lei Huang, Weijiang Yu, Weitao Ma, Weihong Zhong, Zhangyin Feng, Haotian Wang, Qianglong Chen, Weihua Peng, Xiaocheng Feng, Bing Qin, Ting Liu Abstract: The emergence of large language models (LLMs) has marked a significant breakthrough in natural language processing (NLP), leading
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Original Paper: https://arxiv.org/abs/2309.08589 By: Hugh Zhang, David C. Parkes Abstract: Large language models have astounded the world with fascinating new capabilities. However, they currently lack the ability to teach themselves new skills, relying instead on large amounts of human-generated training data. We introduce SECToR (Self-Education
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Original Paper: https://arxiv.org/abs/2311.04155 By: Jiale Cheng, Xiao Liu, Kehan Zheng, Pei Ke, Hongning Wang, Yuxiao Dong, Jie Tang, Minlie Huang Abstract: Large language models (LLMs) have shown impressive success in various applications. However, these models are often not well aligned with human intents, which calls
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Original Paper: https://arxiv.org/abs/2310.17419 By: You-Ming Chang, Chen Yeh, Wei-Chen Chiu, Ning Yu Abstract: Deep generative models can create remarkably photorealistic fake images while raising concerns about misinformation and copyright infringement, known as deepfake threats. Deepfake detection technique is developed to distinguish between real and fake
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Original Paper: https://arxiv.org/abs/2311.01011 By: Sam Toyer, Olivia Watkins, Ethan Adrian Mendes, Justin Svegliato, Luke Bailey, Tiffany Wang, Isaac Ong, Karim Elmaaroufi, Pieter Abbeel, Trevor Darrell, Alan Ritter, Stuart Russell Abstract: While Large Language Models (LLMs) are increasingly being used in real-world applications, they remain vulnerable
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Original Paper: https://arxiv.org/abs/2311.00172 By: Jinhwa Kim, Ali Derakhshan, Ian G. Harris Abstract: Large Language Models' safety remains a critical concern due to their vulnerability to adversarial attacks, which can prompt these systems to produce harmful responses. In the heart of these systems lies a
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Original Paper: https://arxiv.org/abs/2310.18867 By: Rubaba Amyeen Abstract: Question generation has numerous applications in the educational context. Question generation can prove helpful for students when reviewing content and testing themselves. Furthermore, a question generation model can aid teachers by lessening the burden of creating assessments and
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Original Paper: https://arxiv.org/abs/2308.13259 By: Keheng Wang, Feiyu Duan, Sirui Wang, Peiguang Li, Yunsen Xian, Chuantao Yin, Wenge Rong, Zhang Xiong Abstract: Equipped with Chain-of-Thought (CoT), Large language models (LLMs) have shown impressive reasoning ability in various downstream tasks. Even so, suffering from hallucinations and the
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Original Paper: https://arxiv.org/abs/2109.06977 By: Vivian Liu, Lydia B. Chilton Abstract: Text-to-image generative models are a new and powerful way to generate visual artwork. However, the open-ended nature of text as interaction is double-edged; while users can input anything and have access to an infinite range
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Original Paper: https://arxiv.org/pdf/2310.14735 By: Banghao Chen, Zhaofeng Zhang, Nicolas Langrené, Shengxin Zhu Abstract: This paper delves into the pivotal role of prompt engineering in unleashing the capabilities of Large Language Models (LLMs). Prompt engineering is the process of structuring input text for LLMs and is
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Original Paper: https://arxiv.org/abs/2301.08721 By: Zhoujun Cheng, Jungo Kasai, Tao Yu Abstract: Performing inference on large volumes of samples with large language models (LLMs) can be computationally and financially costly in industry and real-world use. We propose batch prompting, a simple yet effective prompting approach that
