Summary of Llm-topla: Efficient Llm Ensemble by Maximising Diversity, By Selim Furkan Tekin et al.
LLM-TOPLA: Efficient LLM Ensemble by Maximising Diversity
by Selim Furkan Tekin, Fatih Ilhan, Tiansheng Huang, Sihao Hu, Ling Liu
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 This paper presents LLM-TOPLA, a novel approach to combine large language models (LLMs) during training or inference. The method optimizes diversity among component LLMs using the focal diversity metric and develops an ensemble pruning algorithm to select top-performing sub-ensembles. The learn-to-ensemble approach resolves output inconsistencies among components, leading to improved performance on four benchmarks: MMLU, GSM8k, SearchQA, and XSum. Compared to state-of-the-art methods Mixtral and MoreAgent, LLM-TOPLA achieves significant gains in accuracy (2.2% on MMLU) and F1 scores (3.9x on SearchQA). The code and dataset are available at https://github.com/git-disl/llm-topla. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper presents a new way to combine big language models together. It’s called LLM-TOPLA, and it helps the models work better by making sure they’re diverse and not saying the same thing. The method picks the best parts from different models and learns how to fix any inconsistencies in their answers. This leads to better results on several tests: MMLU, GSM8k, SearchQA, and XSum. LLM-TOPLA even beats some other top-performing methods by a lot! You can find the code and data used in this research at https://github.com/git-disl/llm-topla. |
Keywords
» Artificial intelligence » Inference » Pruning
