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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/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/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/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/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
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Original Paper: https://arxiv.org/abs/2309.06553 By: Hao Sun, Alihan Hüyük, Mihaela van der Schaar Abstract: In this study, we aim to enhance the arithmetic reasoning ability of Large Language Models (LLMs) through zero-shot prompt optimization. We identify a previously overlooked objective of query dependency in such optimization
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Original Paper: https://arxiv.org/abs/2403.04123 By: Devjeet Roy, Xuchao Zhang, Rashi Bhave, Chetan Bansal, Pedro Las-Casas, Rodrigo Fonseca, Saravan Rajmohan Abstract: The growing complexity of cloud based software systems has resulted in incident management becoming an integral part of the software development lifecycle. Root cause analysis (RCA)
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Original Paper: https://arxiv.org/abs/2403.04121 By: Subbarao Kambhampati Abstract While humans sometimes do show the capability of correcting their own erroneous guesses with self-critiquing, there seems to be no basis for that assumption in the case of LLMs. Summary Notes Figure: An informal account of viewing LLM