Summary of Toward Self-improvement Of Llms Via Imagination, Searching, and Criticizing, by Ye Tian and Baolin Peng and Linfeng Song and Lifeng Jin and Dian Yu and Haitao Mi and Dong Yu
Toward Self-Improvement of LLMs via Imagination, Searching, and Criticizing
by Ye Tian, Baolin Peng, Linfeng Song, Lifeng Jin, Dian Yu, Haitao Mi, Dong Yu
First submitted to arxiv on: 18 Apr 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 A new large language model (LLM) has been proposed to improve its capabilities through self-correction and self-learning. The current approaches for augmenting LLMs’ reasoning abilities are limited by data availability and quality. To address this, the authors introduce AlphaLLM, a system that integrates Monte Carlo Tree Search (MCTS) with LLMs to establish a self-improving loop. This allows LLMs to refine their outputs and learn from self-assessed rewards without additional annotations. The system consists of a prompt synthesis component, an efficient MCTS approach tailored for language tasks, and a trio of critic models for precise feedback. Experimental results in mathematical reasoning tasks demonstrate that AlphaLLM significantly enhances the performance of LLMs without additional annotations, showing the potential for self-improvement in LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are very good at doing certain things, but they struggle with complex thinking and planning. Researchers have tried different ways to make them better, like using special prompts or fine-tuning them with more data. However, these methods aren’t perfect because they rely on having a lot of high-quality data. A new approach called AlphaLLM tries to fix this problem by letting the LLM improve itself through self-correction and learning. This means the model can make mistakes, learn from those mistakes, and get better over time without needing more training data. The creators of AlphaLLM tested it on some math problems and found that it worked really well! |
Keywords
» Artificial intelligence » Fine tuning » Large language model » Prompt
