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In the In-Context Learning (ICL) setup, various forms of label biases can manifest.
Language models are few-shot learners
Brown, T.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J. D.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; et al. 2020 · 1901
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BoolQ: Exploring the surprising difficulty of natural yes/no questions
Clark, C.; Lee, K.; Chang, M.-W.; Kwiatkowski, T.; Collins, M.; and Toutanova, K. 2019 · 1905
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The third pascal recognizing textual entailment challenge
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The role of the crowd in countering misinformation: A case study of the COVID-19 infodemic
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What Makes Good In-Context Examples for GPT- 3 3 ?
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Calibrate Before Use: Improving Few-Shot Performance of Language Models
Zhao, T. Z.; Wallace, E.; Feng, S.; Klein, D.; and Singh, S. 2021 · 2021
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Fantastically Ordered Prompts and Where to Find Them: Overcoming Few-Shot Prompt Order Sensitivity
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PEFT: State-of-the-art Parameter-Efficient Fine-Tuning methods
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Falcon-40B: an open large language model with state-of-the-art performance
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Mitigating Label Biases for In-context Learning
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OpenAssistant Conversations–Democratizing Large Language Model Alignment
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Introducing MPT-7B: A New Standard for Open-Source, Commercially Usable LLMs
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A Comprehensive Overview of Large Language Models
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Text Classification via Large Language Models
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Investigating the Learning Behaviour of In-context Learning: A Comparison with Supervised Learning
Wang, X.; Wang, Y.; Xu, C.; Geng, X.; Zhang, B.; Tao, C.; Rudzicz, F.; Mercer, R. E.; and Jiang, D. 2023 · 2023
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