Fine-tuning BERT-class models on LLM-generated labels consistently produces less accurate, more unstable classifiers that plateau early, and common fixes only partially help.
Deep Learning for Economists
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abstract
Deep learning provides powerful methods to impute structured information from large-scale, unstructured text and image datasets. For example, economists might wish to detect the presence of economic activity in satellite images, or to measure the topics or entities mentioned in social media, the congressional record, or firm filings. This review introduces deep neural networks, covering methods such as classifiers, regression models, generative AI, and embedding models. Applications include classification, document digitization, record linkage, and methods for data exploration in massive scale text and image corpora. When suitable methods are used, deep learning models can be cheap to tune and can scale affordably to problems involving millions or billions of data points.. The review is accompanied by a companion website, EconDL, with user-friendly demo notebooks, software resources, and a knowledge base that provides technical details and additional applications.
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cs.CL 1years
2025 1verdicts
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Feeding LLM Annotations to BERT Classifiers at Your Own Risk
Fine-tuning BERT-class models on LLM-generated labels consistently produces less accurate, more unstable classifiers that plateau early, and common fixes only partially help.