A fine-tuned GPT-3.5 Turbo model predicts the direction of held-out food-policy experiments with 79% accuracy, but only 55% on preregistered unpublished studies.
Template-Based Named Entity Recognition Using BART
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abstract
There is a recent interest in investigating few-shot NER, where the low-resource target domain has different label sets compared with a resource-rich source domain. Existing methods use a similarity-based metric. However, they cannot make full use of knowledge transfer in NER model parameters. To address the issue, we propose a template-based method for NER, treating NER as a language model ranking problem in a sequence-to-sequence framework, where original sentences and statement templates filled by candidate named entity span are regarded as the source sequence and the target sequence, respectively. For inference, the model is required to classify each candidate span based on the corresponding template scores. Our experiments demonstrate that the proposed method achieves 92.55% F1 score on the CoNLL03 (rich-resource task), and significantly better than fine-tuning BERT 10.88%, 15.34%, and 11.73% F1 score on the MIT Movie, the MIT Restaurant, and the ATIS (low-resource task), respectively.
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cs.AI 1years
2024 1verdicts
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Leveraging LLMs for Predictive Insights in Food Policy and Behavioral Interventions
A fine-tuned GPT-3.5 Turbo model predicts the direction of held-out food-policy experiments with 79% accuracy, but only 55% on preregistered unpublished studies.