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Recent work has shown that language models scaled to billions of parameters,such as GPT-3, perform remarkably well in zero-shot and few-shot scenarios. Inthis work, we experiment with zero-shot models in the legal case entailmenttask of the COLIEE 2022 competition. Our experiments show that scaling thenumber of parameters in a language model improves the F1 score of our previouszero-shot result by more than 6 points, suggesting that stronger zero-shotcapability may be a characteristic of larger models, at least for this task.Our 3B-parameter zero-shot model outperforms all models, including ensembles,in the COLIEE 2021 test set and also achieves the best performance of a singlemodel in the COLIEE 2022 competition, second only to the ensemble composed ofthe 3B model itself and a smaller version of the same model. Despite thechallenges posed by large language models, mainly due to latency constraints inreal-time applications, we provide a demonstration of our zero-shot monoT5-3bmodel being used in production as a search engine, including for legaldocuments. The code for our submission and the demo of our system are availableat https://github.com/neuralmind-ai/coliee andhttps://neuralsearchx.neuralmind.ai, respectively.

Billions of Parameters Are Worth More Than In-domain Training Data: A case study in the Legal Case Entailment Task