Dipper: Diversity in Prompts for Producing Large Language Model Ensembles in Reasoning tasks

• URL: http://arxiv.org/abs/2412.15238v1
• Date: Thu, 12 Dec 2024 17:49:05 GMT
• Title: Dipper: Diversity in Prompts for Producing Large Language Model Ensembles in Reasoning tasks
• Authors: Gregory Kang Ruey Lau, Wenyang Hu, Diwen Liu, Jizhuo Chen, See-Kiong Ng, Bryan Kian Hsiang Low,
• Abstract summary: Inference-time methods to boost Large Language Models performance have been shown effective in past works, though they largely rely on sequential queries. We propose a novel, training-free LLM ensemble framework where a single model is fed an optimized, diverse set of prompts in parallel. We empirically demonstrate that our method leads to significant gains on math reasoning tasks, e.g., on MATH.
• Score: 39.820621967837205
• License:
• Abstract: Large Language Models still encounter substantial challenges in reasoning tasks, especially for smaller models, which many users may be restricted to due to resource constraints (e.g. GPU memory restrictions). Inference-time methods to boost LLM performance, such as prompting methods to invoke certain reasoning pathways in responses, have been shown effective in past works, though they largely rely on sequential queries. The ensemble method, which consists of multiple constituent models running in parallel, is a promising approach to achieving better inference-time performance, especially given recent developments that enabled significant speed-ups in LLM batch inference. In this work, we propose a novel, training-free LLM ensemble framework where a single LLM model is fed an optimized, diverse set of prompts in parallel, effectively producing an ensemble at inference time to achieve performance improvement in reasoning tasks. We empirically demonstrate that our method leads to significant gains on math reasoning tasks, e.g., on MATH, where our ensemble consisting of a few small models (e.g., three Qwen2-MATH-1.5B-it models) can outperform a larger model (e.g., Qwen2-MATH-7B-it).

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