Summary of Litesearch: Efficacious Tree Search For Llm, by Ante Wang et al.
LiteSearch: Efficacious Tree Search for LLM
by Ante Wang, Linfeng Song, Ye Tian, Baolin Peng, Dian Yu, Haitao Mi, Jinsong Su, Dong Yu
First submitted to arxiv on: 29 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper proposes a novel guided tree search algorithm for boosting Large Language Model (LLM) performance on complex mathematical reasoning tasks. The algorithm, called dynamic node selection and exploration budget calculation, is designed to efficiently explore the search space by considering both the current progress towards the final answer (history) and the guidance from a value network trained without step-wise annotations (future). This approach aims to reduce wastefulness in tree search algorithms, which often require excessive computational resources. Experimental results on GSM8K and TabMWP datasets show that this method achieves competitive performance while significantly reducing computational costs compared to baseline methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created a new way to make language models better at math problems. They wanted to find a way to use tree search algorithms, which are good at solving complex math problems, but don’t waste too much computer power doing it. To do this, they came up with an algorithm that looks at how well the model is doing right now and what it might do in the future, based on some predictions. This helps the model make better choices about where to look for answers. The results show that this new way of searching works just as well as other methods, but uses much less computer power. |
Keywords
» Artificial intelligence » Boosting » Large language model
