  In recent years, large language models (LLMs) have achieved strong
performance on benchmark tasks, especially in zero or few-shot settings.
However, these benchmarks often do not adequately address the challenges posed
in the real-world, such as that of hierarchical classification. In order to
address this challenge, we propose refactoring conventional tasks on
hierarchical datasets into a more indicative long-tail prediction task. We
observe LLMs are more prone to failure in these cases. To address these
limitations, we propose the use of entailment-contradiction prediction in
conjunction with LLMs, which allows for strong performance in a strict
zero-shot setting. Importantly, our method does not require any parameter
updates, a resource-intensive process and achieves strong performance across
multiple datasets.
