@inproceedings{guo-etal-2025-ped,
    title = "{M}-Ped: Multi-Prompt Ensemble Decoding for Large Language Models",
    author = "Guo, Jiaxin  and
      Wei, Daimeng  and
      Luo, Yuanchang  and
      Shang, Hengchao  and
      Li, Zongyao  and
      Yang, Jinlong  and
      Wu, Zhanglin  and
      Rao, Zhiqiang  and
      Tao, Shimin  and
      Yang, Hao",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/name-variant-enfa-fane/2025.findings-emnlp.906/",
    doi = "10.18653/v1/2025.findings-emnlp.906",
    pages = "16693--16711",
    ISBN = "979-8-89176-335-7",
    abstract = "With the widespread application of Large Language Models (LLMs) in the field of Natural Language Processing (NLP), enhancing their performance has become a research hotspot. This paper presents a novel multi-prompt ensemble decoding approach designed to bolster the generation quality of LLMs by leveraging the aggregation of outcomes from multiple prompts. Given a unique input X, we submit n variations of prompts with X to LLMs in batch mode to decode and derive probability distributions. For each token prediction, we calculate the ensemble probability by averaging the n probability distributions within the batch, utilizing this aggregated probability to generate the token. This technique is dubbed Inner-Batch Ensemble. To facilitate efficient batch inference, we implement a Left-Padding strategy to maintain uniform input lengths across the n prompts. Through extensive experimentation on diverse NLP tasks, including code generation, text simplification and machine translation, we demonstrate the efficacy of our method in enhancing LLM performance. The results show substantial improvements in pass@k rates, LENS metrics and BLEU scores over conventional methods."
}