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Large Language Models (LLMs) exhibit substantial capabilities yet encounter challenges, including hallucination, outdated knowledge, and untraceable reasoning processes.
Language models are few-shot learners
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Textattack: A framework for adversarial attacks, data augmentation, and adversarial training in nlp
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Generation-augmented retrieval for open-domain question answering
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Infobert: Improving robustness of language models from an information theoretic perspective
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Semantic parsing on freebase from question-answer pairs
Jonathan Berant, Andrew Chou, Roy Frostig, and Percy Liang. 2013 · 2013
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Explaining and harnessing adversarial examples
Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. 2014 · 2014
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Adversarial machine learning at scale
Alexey Kurakin, Ian Goodfellow, and Samy Bengio. 2016 · 2016
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Adversarial training methods for semi-supervised text classification
Takeru Miyato, Andrew M Dai, and Ian Goodfellow. 2016 · 2016
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Reading wikipedia to answer open-domain questions
Danqi Chen, Adam Fisch, Jason Weston, and Antoine Bordes. 2017 · 2017
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Adversarial examples for evaluating reading comprehension systems
Robin Jia and Percy Liang. 2017 · 2017
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Triviaqa: A large scale distantly supervised challenge dataset for reading comprehension
Mandar Joshi, Eunsol Choi, Daniel S Weld, and Luke Zettlemoyer. 2017 · 2017
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu. 2017 · 2017
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An overview of multi-task learning in deep neural networks
Sebastian Ruder. 2017 · 2017
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Adversarial training for relation extraction
Yi Wu, David Bamman, and Stuart Russell. 2017 · 2017
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Robust multilingual part-of-speech tagging via adversarial training
Michihiro Yasunaga, Jungo Kasai, and Dragomir Radev. 2017 · 2017
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Natural questions: a benchmark for question answering research
Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, Michael Collins, Ankur Parikh, Chris Alberti, Danielle Epstein, Illia Polosukhin, Jacob Devlin, Kenton Lee, et al. 2019 · 2019
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Retrieval-augmented generation for knowledge-intensive nlp tasks
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al. 2020 · 2020
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Neftune: Noisy embeddings improve instruction finetuning
Neel Jain, Ping-yeh Chiang, Yuxin Wen, John Kirchenbauer, Hong-Min Chu, Gowthami Somepalli, Brian R Bartoldson, Bhavya Kailkhura, Avi Schwarzschild, Aniruddha Saha, et al. 2023 · 2023
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Large language models struggle to learn long-tail knowledge
Nikhil Kandpal, Haikang Deng, Adam Roberts, Eric Wallace, and Colin Raffel. 2023 · 2023
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Knowledge graph contrastive learning based on relation-symmetrical structure
Ke Liang, Yue Liu, Sihang Zhou, Wenxuan Tu, Yi Wen, Xihong Yang, Xiangjun Dong, and Xinwang Liu. 2023 · 2023
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Ra-dit: Retrieval-augmented dual instruction tuning
Xi Victoria Lin, Xilun Chen, Mingda Chen, Weijia Shi, Maria Lomeli, Rich James, Pedro Rodriguez, Jacob Kahn, Gergely Szilvasy, Mike Lewis, et al. 2023 · 2023
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Dropattack: A random dropped weight attack adversarial training for natural language understanding
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Recent advances in adversarial training for adversarial robustness
Tao Bai, Jinqi Luo, Jun Zhao, Bihan Wen, and Qian Wang. 2021 · 2021
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Achieving model robustness through discrete adversarial training
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Selection-inference: Exploiting large language models for interpretable logical reasoning
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Large language models with controllable working memory
Daliang Li, Ankit Singh Rawat, Manzil Zaheer, Xin Wang, Michal Lukasik, Andreas Veit, Felix Yu, and Sanjiv Kumar. 2022 · 2022
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Jinze Bai, Shuai Bai, Yunfei Chu, Zeyu Cui, Kai Dang, Xiaodong Deng, Yang Fan, Wenbin Ge, Yu Han, Fei Huang, et al. 2023 · 2023
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Sparks of artificial general intelligence: Early experiments with gpt-4
Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, et al. 2023 · 2023
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Benchmarking large language models in retrieval-augmented generation
Jiawei Chen, Hongyu Lin, Xianpei Han, and Le Sun. 2023 · 2023
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Shiwen Ni, Jiawen Li, Min Yang, and Hung-Yu Kao. 2023 · 2023
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In chatgpt we trust? measuring and characterizing the reliability of chatgpt
Xinyue Shen, Zeyuan Chen, Michael Backes, and Yang Zhang. 2023 · 2023
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Large language models can be easily distracted by irrelevant context
Freda Shi, Xinyun Chen, Kanishka Misra, Nathan Scales, David Dohan, Ed H Chi, Nathanael Schärli, and Denny Zhou. 2023 · 2023
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Nomiracl: Knowing when you don’t know for robust multilingual retrieval-augmented generation
Nandan Thakur, Luiz Bonifacio, Xinyu Zhang, Odunayo Ogundepo, Ehsan Kamalloo, David Alfonso-Hermelo, Xiaoguang Li, Qun Liu, Boxing Chen, Mehdi Rezagholizadeh, et al. 2023 · 2023
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Llama 2: Open foundation and fine-tuned chat models
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, Shruti Bhosale, et al. 2023 · 2023
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Alcuna: large language models meet new knowledge
Xunjian Yin, Baizhou Huang, and Xiaojun Wan. 2023 · 2023
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Making retrieval-augmented language models robust to irrelevant context
Ori Yoran, Tomer Wolfson, Ori Ram, and Jonathan Berant. 2023 · 2023
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Chain-of-note: Enhancing robustness in retrieval-augmented language models
Wenhao Yu, Hongming Zhang, Xiaoman Pan, Kaixin Ma, Hongwei Wang, and Dong Yu. 2023 · 2023
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Large language models for information retrieval: A survey
Yutao Zhu, Huaying Yuan, Shuting Wang, Jiongnan Liu, Wenhan Liu, Chenlong Deng, Zhicheng Dou, and Ji-Rong Wen. 2023 · 2023
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Universal and transferable adversarial attacks on aligned language models
Andy Zou, Zifan Wang, J Zico Kolter, and Matt Fredrikson. 2023 · 2023
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