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This paper assesses the accuracy, reliability and bias of the Large Language Model (LLM) ChatGPT-4 on the text analysis task of classifying the political affiliation of a Twitter poster based on the content of a tweet.
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arXiv preprint arXiv:2303.12712
S Bubeck, et al., Sparks of Artificial General Intelligence: Early experiments with GPT-4 · 2023
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arXiv preprint arXiv:2303.12057
PY Wu, JA Tucker, J Nagler, S Messing, Large Language Models Can Be Used to Estimate the Ideologies of Politicians in a Zero-Shot Learning Setting · 2023
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arXiv preprint arXiv:2303.15056
F Gilardi, M Alizadeh, M Kubli, Chatgpt outperforms crowd-workers for text-annotation tasks · 2023
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J Wei, et al., Emergent abilities of large language models · 2022
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arXiv preprint arXiv:2205.11916
T Kojima, SS Gu, M Reid, Y Matsuo, Y Iwasawa, Large language models are zero-shot reasoners · 2022
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arXiv preprint arXiv:2209.06899
LP Argyle, et al., Out of One, Many: Using Language Models to Simulate Human Samples · 2022
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arXiv preprint arXiv:2302.04023
Y Bang, et al., A multitask, multilingual, multimodal evaluation of chatgpt on reasoning, hallucination, and interactivity · 2023
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A Palmer, A Spirling, Large Language Models Can Argue in Convincing and Novel Ways About Politics: Evidence from Experiments and Human Judgement, (Working paper), Technical report (2023)
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OSF Preprints
M Reiss, Testing the Reliability of ChatGPT for Text Annotation and Classification: A Cautionary Remark · 2023
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