research-papers
Original Paper: https://arxiv.org/abs/2312.14091 By: Hayk Manukyan, Andranik Sargsyan, Barsegh Atanyan, Zhangyang Wang, Shant Navasardyan, Humphrey Shi Abstract: Recent progress in text-guided image inpainting, based on the unprecedented success of text-to-image diffusion models, has led to exceptionally realistic and visually plausible results. However, there is still
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Original Paper: https://arxiv.org/abs/2312.03724 By: Junyuan Hong, Jiachen T. Wang, Chenhui Zhang, Zhangheng Li, Bo Li, Zhangyang Wang Abstract: Large Language Models (LLMs) have emerged as dominant tools for various tasks, particularly when tailored for a specific target by prompt tuning. Nevertheless, concerns surrounding data privacy
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Original Paper: https://arxiv.org/abs/2310.18961 By: Qihang Zhou, Guansong Pang, Yu Tian, Shibo He, Jiming Chen Abstract: Zero-shot anomaly detection (ZSAD) requires detection models trained using auxiliary data to detect anomalies without any training sample in a target dataset. It is a crucial task when training data
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Original Paper: https://arxiv.org/abs/2403.10131 By: Tianjun Zhang, Shishir G. Patil, Naman Jain, Sheng Shen, Matei Zaharia, Ion Stoica, Joseph E. Gonzalez Abstract: Pretraining Large Language Models (LLMs) on large corpora of textual data is now a standard paradigm. When using these LLMs for many downstream applications,
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Original Paper: https://arxiv.org/abs/2403.09611 By: Brandon McKinzie, Zhe Gan, Jean-Philippe Fauconnier, Sam Dodge, Bowen Zhang, Philipp Dufter, Dhruti Shah, Xianzhi Du, Futang Peng, Floris Weers, Anton Belyi, Haotian Zhang, Karanjeet Singh, Doug Kang, Ankur Jain, Hongyu Hè, Max Schwarzer, Tom Gunter, Xiang Kong, Aonan Zhang, Jianyu
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Original Paper: https://arxiv.org/abs/2311.08364 By: Rui Pan, Shuo Xing, Shizhe Diao, Wenhe Sun, Xiang Liu, Kashun Shum, Renjie Pi, Jipeng Zhang, Tong Zhang Abstract: Since the emergence of large language models, prompt learning has become a popular method for optimizing and customizing these models. Special prompts,
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Original Paper: https://arxiv.org/html/2403.20127v1 AI has come a long way, with tools like GPT-3 making big waves. They're super useful, but there's a downside: they can create fake news or copy someone else's work. To stop this, experts have come
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Original Paper: https://arxiv.org/abs/2403.08950 By: Michael Desmond, Michelle Brachman Abstract: Interaction with Large Language Models (LLMs) is primarily carried out via prompting. A prompt is a natural language instruction designed to elicit certain behaviour or output from a model. In theory, natural language prompts enable non-experts
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Original Paper: https://arxiv.org/abs/2311.18231 By: Hantao Yao, Rui Zhang, Changsheng Xu Abstract: Prompt tuning represents a valuable technique for adapting pre-trained visual-language models (VLM) to various downstream tasks. Recent advancements in CoOp-based methods propose a set of learnable domain-shared or image-conditional textual tokens to facilitate the
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Original Paper: https://arxiv.org/abs/2305.13860 By: Yi Liu, Gelei Deng, Zhengzi Xu, Yuekang Li, Yaowen Zheng, Ying Zhang, Lida Zhao, Tianwei Zhang, Kailong Wang, Yang Liu Abstract: Large Language Models (LLMs), like ChatGPT, have demonstrated vast potential but also introduce challenges related to content constraints and potential
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Original Paper: https://arxiv.org/abs/2403.05313 By: Zihao Wang, Anji Liu, Haowei Lin, Jiaqi Li, Xiaojian Ma, Yitao Liang Abstract: We explore how iterative revising a chain of thoughts with the help of information retrieval significantly improves large language models' reasoning and generation ability in long-horizon generation
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Original Paper: https://arxiv.org/abs/2312.14197 By: Jingwei Yi, Yueqi Xie, Bin Zhu, Emre Kiciman, Guangzhong Sun, Xing Xie, Fangzhao Wu Abstract: The integration of large language models (LLMs) with external content has enabled more up-to-date and wide-ranging applications of LLMs, such as Microsoft Copilot. However, this integration