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Large Language Models (LLMs) have demonstrated impressive zero-shot capabilities and versatility in NLP tasks, however they sometimes fail to maintain crucial invariances for specific tasks.
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, et al. 2020 · 1901
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Roberta: A robustly optimized bert pretraining approach
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Summeval: Re-evaluating summarization evaluation
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Distilling the knowledge in a neural network
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On calibration of modern neural networks
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Exploring the limits of transfer learning with a unified text-to-text transformer
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Prompt programming for large language models: Beyond the few-shot paradigm
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Finetuned language models are zero-shot learners
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Calibrate before use: Improving few-shot performance of language models
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Gender bias and stereotypes in large language models
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Mitigating word bias in zero-shot prompt-based classifiers
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The larger they are, the harder they fail: Language models do not recognize identifier swaps in python
Antonio Valerio Miceli Barone, Fazl Barez, Shay B. Cohen, and Ioannis Konstas. 2023 · 2023
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Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, et al. 2022 · 2022
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Open-llm-leaderboard-report
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Large language models sensitivity to the order of options in multiple-choice questions
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Llama 2: Open foundation and fine-tuned chat models
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Large language models are not fair evaluators
Peiyi Wang, Lei Li, Liang Chen, Zefan Cai, Dawei Zhu, Binghuai Lin, Yunbo Cao, Qi Liu, Tianyu Liu, and Zhifang Sui. 2023 · 2023
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