Summary of Dipper: Diversity in Prompts For Producing Large Language Model Ensembles in Reasoning Tasks, by Gregory Kang Ruey Lau et al.
Dipper: Diversity in Prompts for Producing Large Language Model Ensembles in Reasoning tasks
by Gregory Kang Ruey Lau, Wenyang Hu, Diwen Liu, Jizhuo Chen, See-Kiong Ng, Bryan Kian Hsiang Low
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multiagent Systems (cs.MA)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel training-free Large Language Model (LLM) ensemble framework is proposed to boost inference-time performance on reasoning tasks, particularly for smaller models constrained by resource limitations. The approach feeds a single LLM an optimized set of prompts in parallel, generating an ensemble at inference time. Experimental results demonstrate significant gains on math reasoning tasks, such as MATH, where a few small models outperform a larger one. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about making computers smarter and better at solving problems. It’s about using special language models that can understand and generate human-like text. The problem is that these models are not very good at doing math and other complex tasks. To solve this, scientists propose a new way to use multiple small models together to get better results. This approach doesn’t need any extra training or data, it just uses the same model with different prompts. The experiments show that this method can make smaller models do math problems as well as bigger models. |
Keywords
» Artificial intelligence » Inference » Large language model
