@inproceedings{bao-etal-2025-exploring,
title = "Exploring Hybrid Sampling Inference for Aspect-based Sentiment Analysis",
author = "Bao, Xiaoyi and
Qiang, Minjie and
Gu, Jinghang and
Wang, Zhongqing and
Huang, Chu-Ren",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-naacl.236/",
doi = "10.18653/v1/2025.findings-naacl.236",
pages = "4199--4210",
ISBN = "979-8-89176-195-7",
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 \textbf{Hybrid Sampling} that combining both multiple and single sampling to greatly reduce the cost of multiple sampling without sacrificing performance. \textbf{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."
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<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.</abstract>
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%0 Conference Proceedings
%T Exploring Hybrid Sampling Inference for Aspect-based Sentiment Analysis
%A Bao, Xiaoyi
%A Qiang, Minjie
%A Gu, Jinghang
%A Wang, Zhongqing
%A Huang, Chu-Ren
%Y Chiruzzo, Luis
%Y Ritter, Alan
%Y Wang, Lu
%S Findings of the Association for Computational Linguistics: NAACL 2025
%D 2025
%8 April
%I Association for Computational Linguistics
%C Albuquerque, New Mexico
%@ 979-8-89176-195-7
%F bao-etal-2025-exploring
%X 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.
%R 10.18653/v1/2025.findings-naacl.236
%U https://aclanthology.org/2025.findings-naacl.236/
%U https://doi.org/10.18653/v1/2025.findings-naacl.236
%P 4199-4210
Markdown (Informal)
[Exploring Hybrid Sampling Inference for Aspect-based Sentiment Analysis](https://aclanthology.org/2025.findings-naacl.236/) (Bao et al., Findings 2025)
ACL