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Large language models are extensively applied across a wide range of tasks, such as customer support, content creation, educational tutoring, and providing financial guidance.
Dropedge: Towards deep graph convolutional networks on node classification
Yu Rong, Wenbing Huang, Tingyang Xu, and Junzhou Huang. 2020 · 1907
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Rational decisions
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Some effective techniques for naive bayes text classification
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Adam: A method for stochastic optimization
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SGDR: Stochastic Gradient Descent with Warm Restarts
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Graph Attention Networks
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
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Decoupled Weight Decay Regularization
Ilya Loshchilov and Frank Hutter. 2019 · 2019
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Language models are few-shot learners
Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Neelakantan Arvind, Shyam Pranav, Sastry Girish, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey 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. 2020a · 2020
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Chain-of-verification reduces hallucination in large language models
Shehzaad Dhuliawala, Mojtaba Komeili, Jing Xu, Roberta Raileanu, Xian Li, Asli Celikyilmaz, and Jason Weston. 2023 · 2023
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Trapping LLM hallucinations using tagged context prompts
Philip Feldman, James R. Foulds, and Shimei Pan. 2023 · 2023
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Vicunaner: Zero/few-shot named entity recognition using vicuna
Bin Ji. 2023 · 2023
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Towards mitigating LLM hallucination via self reflection
Ziwei Ji, Tiezheng Yu, Yan Xu, Nayeon Lee, Etsuko Ishii, and Pascale Fung. 2023b · 2023
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ChatGPT for good? On opportunities and challenges of large language models for education
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Exploring the limits of transfer learning with a unified text-to-text transformer
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Transformers: State-of-the-art natural language processing
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Forget Me Not: Reducing Catastrophic Forgetting for Domain Adaptation in Reading Comprehension
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DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION
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GAEN: Graph Attention Evolving Networks
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Gpt-neox-20b: An open-source autoregressive language model
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TruthfulQA: Measuring How Models Mimic Human Falsehoods
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Zero-resource hallucination prevention for large language models
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SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models
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Knowledge injection to counter large language model (LLM) hallucination
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An important next step on our ai journey
Sundar Pichai. 2023 · 2023
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Llama 2: Open Foundation and Fine-Tuned Chat Models
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Neeraj Varshney, Wenlin Yao, Hongming Zhang, Jianshu Chen, and Dong Yu. 2023 · 2023
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Hallucination detection for generative large language models by bayesian sequential estimation
Xiaohua Wang, Yuliang Yan, Longtao Huang, Xiaoqing Zheng, and Xuanjing Huang. 2023 · 2023
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Learning to trust your feelings: Leveraging self-awareness in llms for hallucination mitigation
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Hallucination is inevitable: An innate limitation of large language models
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