@inproceedings{ruan-etal-2025-g2,
title = "G2: Guided Generation for Enhanced Output Diversity in {LLM}s",
author = "Ruan, Zhiwen and
Li, Yixia and
Liu, Yefeng and
Chen, Yun and
Luo, Weihua and
Li, Peng and
Liu, Yang and
Chen, Guanhua",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.713/",
pages = "14127--14145",
ISBN = "979-8-89176-332-6",
abstract = "Large Language Models (LLMs) have demonstrated exceptional performance across diverse natural language processing tasks. However, these models exhibit a critical limitation in output diversity, often generating highly similar content across multiple attempts. This limitation significantly affects tasks requiring diverse outputs, from creative writing to reasoning. Existing solutions, like temperature scaling, enhance diversity by modifying probability distributions but compromise output quality. We propose Guide-to-Generation (G2), a training-free plug-and-play method that enhances output diversity while preserving generation quality. G2 employs a base generator alongside dual Guides, which guide the generation process through decoding-based interventions to encourage more diverse outputs conditioned on the original query. Comprehensive experiments demonstrate that G2 effectively improves output diversity while maintaining an optimal balance between diversity and quality."
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<abstract>Large Language Models (LLMs) have demonstrated exceptional performance across diverse natural language processing tasks. However, these models exhibit a critical limitation in output diversity, often generating highly similar content across multiple attempts. This limitation significantly affects tasks requiring diverse outputs, from creative writing to reasoning. Existing solutions, like temperature scaling, enhance diversity by modifying probability distributions but compromise output quality. We propose Guide-to-Generation (G2), a training-free plug-and-play method that enhances output diversity while preserving generation quality. G2 employs a base generator alongside dual Guides, which guide the generation process through decoding-based interventions to encourage more diverse outputs conditioned on the original query. Comprehensive experiments demonstrate that G2 effectively improves output diversity while maintaining an optimal balance between diversity and quality.</abstract>
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%0 Conference Proceedings
%T G2: Guided Generation for Enhanced Output Diversity in LLMs
%A Ruan, Zhiwen
%A Li, Yixia
%A Liu, Yefeng
%A Chen, Yun
%A Luo, Weihua
%A Li, Peng
%A Liu, Yang
%A Chen, Guanhua
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F ruan-etal-2025-g2
%X Large Language Models (LLMs) have demonstrated exceptional performance across diverse natural language processing tasks. However, these models exhibit a critical limitation in output diversity, often generating highly similar content across multiple attempts. This limitation significantly affects tasks requiring diverse outputs, from creative writing to reasoning. Existing solutions, like temperature scaling, enhance diversity by modifying probability distributions but compromise output quality. We propose Guide-to-Generation (G2), a training-free plug-and-play method that enhances output diversity while preserving generation quality. G2 employs a base generator alongside dual Guides, which guide the generation process through decoding-based interventions to encourage more diverse outputs conditioned on the original query. Comprehensive experiments demonstrate that G2 effectively improves output diversity while maintaining an optimal balance between diversity and quality.
%U https://aclanthology.org/2025.emnlp-main.713/
%P 14127-14145
Markdown (Informal)
[G2: Guided Generation for Enhanced Output Diversity in LLMs](https://aclanthology.org/2025.emnlp-main.713/) (Ruan et al., EMNLP 2025)
ACL
- Zhiwen Ruan, Yixia Li, Yefeng Liu, Yun Chen, Weihua Luo, Peng Li, Yang Liu, and Guanhua Chen. 2025. G2: Guided Generation for Enhanced Output Diversity in LLMs. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 14127–14145, Suzhou, China. Association for Computational Linguistics.