A Simple and Effective Framework for Strict Zero-Shot Hierarchical Classification
Rohan V Bhambhoria, Lei Chen, Xiaodan Zhu · 2023
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 longtail 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.