@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 {\_}{\_}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."
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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