Fetching the paper…
Reading the bibliography…
In-context learning (ICL) and Retrieval-Augmented Generation (RAG) have gained attention for their ability to enhance LLMs' reasoning by incorporating external knowledge but suffer from limited contextual window size, leading to insufficient information injection.
Catastrophic interference in connectionist networks: The sequential learning problem
Michael McCloskey and Neal J. Cohen · 1989
Earlier work this paper cites.
Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, T. J. Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeff Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2005
Earlier work this paper cites.
Unbiased split selection for classification trees based on the gini index
Carolin Strobl, Anne-Laure Boulesteix, and Thomas Augustin · 2007
Earlier work this paper cites.
Detecting large-scale system problems by mining console logs
Wei Xu, Ling Huang, Armando Fox, David Patterson, and Michael I Jordan · 2009
Earlier work this paper cites.
Log clustering based problem identification for online service systems
Qingwei Lin, Hongyu Zhang, Jian-Guang Lou, Yu Zhang, and Xuewei Chen · 2016
Earlier work this paper cites.
Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle · 2016
Earlier work this paper cites.
Deeplog: Anomaly detection and diagnosis from system logs through deep learning
Min Du, Feifei Li, Guineng Zheng, and Vivek Srikumar · 2017
Earlier work this paper cites.
Context-aware representations for knowledge base relation extraction
Daniil Sorokin and Iryna Gurevych · 2017
Earlier work this paper cites.
Learning explanatory rules from noisy data
Richard Evans and Edward Grefenstette · 2018
Earlier work this paper cites.
Probabilistic logic neural networks for reasoning
Meng Qu and Jian Tang · 2019
Earlier work this paper cites.
Simple bert models for relation extraction and semantic role labeling
Peng Shi and Jimmy Lin · 2019
Earlier work this paper cites.
Docred: A large-scale document-level relation extraction dataset
Yuan Yao, Deming Ye, Peng Li, Xu Han, Yankai Lin, Zhenghao Liu, Zhiyuan Liu, Lixin Huang, Jie Zhou, and Maosong Sun · 2019
Earlier work this paper cites.
Robust log-based anomaly detection on unstable log data
Xu Zhang, Yong Xu, Qingwei Lin, Bo Qiao, Hongyu Zhang, Yingnong Dang, Chunyu Xie, Xinsheng Yang, Qian Cheng, Ze Li, et al · 2019
Earlier work this paper cites.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
Earlier work this paper cites.
Retrieval augmented language model pre-training
Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Mingwei Chang · 2020
Earlier work this paper cites.
Temporal logic point processes
Shuang Li, Lu Wang, Ruizhi Zhang, Xiaofu Chang, Xuqin Liu, Yao Xie, Yuan Qi, and Le Song · 2020
Earlier work this paper cites.
Generalizing from a few examples: A survey on few-shot learning
Yaqing Wang, Quanming Yao, James T Kwok, and Lionel M Ni · 2020
Earlier work this paper cites.
Efficient probabilistic logic reasoning with graph neural networks
Yuyu Zhang, Xinshi Chen, Yuan Yang, Arun Ramamurthy, Bo Li, Yuan Qi, and Le Song · 2020
Cited alongside, same era.
Lora: Low-rank adaptation of large language models
Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2021
Cited alongside, same era.
Prefix-tuning: Optimizing continuous prompts for generation
Xiang Lisa Li and Percy Liang · 2021
Cited alongside, same era.
Learning logic rules for document-level relation extraction
Dongyu Ru, Changzhi Sun, Jiangtao Feng, Lin Qiu, Hao Zhou, Weinan Zhang, Yong Yu, and Lei Li · 2021
Cited alongside, same era.
Dwie: An entity-centric dataset for multi-task document-level information extraction
Klim Zaporojets, Johannes Deleu, Chris Develder, and Thomas Demeester · 2021
Cited alongside, same era.
A comprehensive overview of large language models
Humza Naveed, Asad Ullah Khan, Shi Qiu, Muhammad Saqib, Saeed Anwar, Muhammad Usman, Naveed Akhtar, Nick Barnes, and Ajmal Mian · 2023
Later among the works it cites.
Gpt-4: Openai’s generative pre-trained transformer 4 model, 2023
OpenAI · 2023
Later among the works it cites.
Loggpt: Exploring chatgpt for log-based anomaly detection
Jiaxing Qi, Shaohan Huang, Zhongzhi Luan, Shu Yang, Carol Fung, Hailong Yang, Depei Qian, Jing Shang, Zhiwen Xiao, and Zhihui Wu · 2023
Later among the works it cites.
In chatgpt we trust? measuring and characterizing the reliability of chatgpt
Xinyue Shen, Zeyuan Chen, Michael Backes, and Yang Zhang · 2023
Later among the works it cites.
Monte carlo tree search: A review of recent modifications and applications
Maciej Świechowski, Konrad Godlewski, Bartosz Sawicki, and Jacek Mańdziuk · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Improving language models by retrieving from trillions of tokens
Sebastian Borgeaud, Arthur Mensch, Jordan Hoffmann, Trevor Cai, Eliza Rutherford, Katie Millican, George Bm Van Den Driessche, Jean-Baptiste Lespiau, Bogdan Damoc, Aidan Clark, Diego De Las Casas, Aurelia Guy, Jacob Menick, Roman Ring, Tom Hennigan, Saffron Huang, Loren Maggiore, Chris Jones, Albin Cassirer, Andy Brock, Michela Paganini, Geoffrey Irving, Oriol Vinyals, Simon Osindero, Karen Simonyan, Jack Rae, Erich Elsen, and Laurent Sifre · 2022
Cited alongside, same era.
Data distributional properties drive emergent in-context learning in transformers
Stephanie Chan, Adam Santoro, Andrew Lampinen, Jane Wang, Aaditya Singh, Pierre Richemond, James McClelland, and Felix Hill · 2022
Cited alongside, same era.
Rlogic: Recursive logical rule learning from knowledge graphs
Kewei Cheng, Jiahao Liu, Wei Wang, and Yizhou Sun · 2022
Cited alongside, same era.
A survey on in-context learning
Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Zhiyong Wu, Baobao Chang, Xu Sun, Jingjing Xu, and Zhifang Sui · 2022
Cited alongside, same era.
Synchromesh: Reliable code generation from pre-trained language models
Gabriel Poesia, Alex Polozov, Vu Le, Ashish Tiwari, Gustavo Soares, Christopher Meek, and Sumit Gulwani · 2022
Cited alongside, same era.
In-context examples selection for machine translation
Sweta Agrawal, Chunting Zhou, Mike Lewis, Luke Zettlemoyer, and Marjan Ghazvininejad · 2023
Cited alongside, same era.
Temporal logic explanations for dynamic decision systems using anchors and monte carlo tree search
Tzu-Yi Chiu, Jerome Le Ny, and Jean-Pierre David · 2023
Cited alongside, same era.
Enabling mcts explainability for sequential planning through computation tree logic
Ziyan An, Hendrik Baier, Abhishek Dubey, Ayan Mukhopadhyay, and Meiyi Ma · 2024
Closest in time.
A survey on rag meeting llms: Towards retrieval-augmented large language models
Wenqi Fan, Yujuan Ding, Liangbo Ning, Shijie Wang, Hengyun Li, Dawei Yin, Tat-Seng Chua, and Qing Li · 2024
Closest in time.
Large language models are neurosymbolic reasoners
Meng Fang, Shilong Deng, Yudi Zhang, Zijing Shi, Ling Chen, Mykola Pechenizkiy, and Jun Wang · 2024
Closest in time.
Sihao Hu, Tiansheng Huang, and Ling Liu · 2024
Closest in time.
Needlebench: Can llms do retrieval and reasoning in 1 million context window?
Mo Li, Songyang Zhang, Yunxin Liu, and Kai Chen · 2024
Closest in time.
Unifying large language models and knowledge graphs: A roadmap
Shirui Pan, Linhao Luo, Yufei Wang, Chen Chen, Jiapu Wang, and Xindong Wu · 2024
Closest in time.
YaRN: Efficient context window extension of large language models
Bowen Peng, Jeffrey Quesnelle, Honglu Fan, and Enrico Shippole · 2024
Closest in time.
Knowledge graph large language model (kg-llm) for link prediction, 2024
Dong Shu, Tianle Chen, Mingyu Jin, Chong Zhang, Mengnan Du, and Yongfeng Zhang · 2024
Closest in time.
Policy learning with a language bottleneck
Megha Srivastava, Cedric Colas, Dorsa Sadigh, and Jacob Andreas · 2024
Closest in time.
Yuqi Wang, Boran Jiang, Yi Luo, Dawei He, Peng Cheng, and Liangcai Gao · 2024
Closest in time.
UFO: A UI-Focused Agent for Windows OS Interaction
Chaoyun Zhang, Liqun Li, Shilin He, Xu Zhang, Bo Qiao, Si Qin, Minghua Ma, Yu Kang, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang, and Qi Zhang · 2024
Closest in time.