Exploring Hybrid Sampling Inference for Aspect-based Sentiment Analysis

Abstract

As the training of large language models (LLMs) will encounter high computational costs, massive works are now focusing on inference. Their methods can be generally summarised as re-sampling the target multiple times and performing a vote upon the outputs. Despite bringing significant performance improvements, it is a high-cost method that requires multiple sampling with the preset size. In this paper, we propose a simple yet efficient inference strategies named __Hybrid Sampling__ that combining both multiple and single sampling to greatly reduce the cost of multiple sampling without sacrificing performance. __Hybrid Sampling__ could dynamically choose the essential part of generated sequence for multiple sampling and proceed the rest with single sampling, achieving a performance-cost balance. Extensive experiments in several benchmarks underscore the robustness and effectiveness of our proposed Hybrid Sampling and more importantly, it is much faster.