Summary of Explora: Efficient Exemplar Subset Selection For Complex Reasoning, by Kiran Purohit et al.
EXPLORA: Efficient Exemplar Subset Selection for Complex Reasoning
by Kiran Purohit, Venktesh V, Raghuram Devalla, Krishna Mohan Yerragorla, Sourangshu Bhattacharya, Avishek Anand
First submitted to arxiv on: 6 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 The paper proposes a novel algorithm for selecting optimal exemplars in large language models (LLMs) to improve performance on complex reasoning tasks. The proposed method, called EXPLORA, reduces the number of LLM calls by 11% compared to state-of-the-art methods while achieving a significant performance improvement of 12.24%. The paper introduces a novel exploration method designed to estimate the parameters of the scoring function, which evaluates exemplar subsets without incorporating confidence information. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers have developed a way to help large language models learn new tasks by selecting the right examples to practice on. This is important because it can make these models more accurate and efficient. The new method, called EXPLORA, helps find the best examples for a model to learn from, which can improve its performance by 12%. This could be useful in many areas, such as answering complex questions or completing tasks that require reasoning. |
