M-Ped: Multi-Prompt Ensemble Decoding for Large Language Models

TL;DR

This paper introduces M-Ped, a multi-prompt ensemble decoding method that improves large language models' output quality by aggregating multiple prompt outcomes, leading to significant performance gains across various NLP tasks.

Contribution

The paper proposes a novel ensemble decoding technique for LLMs that combines multiple prompt outputs within a batch to enhance generation quality and efficiency.

Findings

Significant BLEU score improvements across tasks

Enhanced pass@$k$ and LENS metrics

Effective batch inference with Left-Padding strategy

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 machine translation, code generation, and text simplification, we demonstrate the efficacy of our method in enhancing LLM performance. The results show substantial improvements in BLEU scores, pass@$k$ rates, and LENS metrics over conventional methods.