Summary of Flaming-hot Initiation with Regular Execution Sampling For Large Language Models, by Weizhe Chen et al.
Flaming-hot Initiation with Regular Execution Sampling for Large Language Models
by Weizhe Chen, Zhicheng Zhang, Guanlin Liu, Renjie Zheng, Wenlei Shi, Chen Dun, Zheng Wu, Xing Jin, Lin Yan
First submitted to arxiv on: 28 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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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 introduces Flaming-hot Initiation with Regular Execution (FIRE) sampling, a method to efficiently find good responses in large language models (LLMs). This approach is particularly effective in reasoning-related tasks, such as math or code, where the goal is to generate correct solutions to specific problems. FIRE sampling enhances inference-time generation quality and benefits training in the alignment stage. The empirical findings show that FIRE improves performance by promoting diversity and analyzing the impact of employing FIRE at different positions within a response. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) are really good at doing lots of things, like answering questions or writing text. But they need high-quality data to learn from. In this paper, scientists invent a new way to find good responses quickly and easily. This helps them make better predictions when generating answers. They test their idea on math problems and coding tasks, where accuracy is important. The results show that their method makes it easier for LLMs to generate correct solutions. |
Keywords
» Artificial intelligence » Alignment » Inference
