Summary of Llm Tree Search, by Dylan Wilson
LLM Tree Search
by Dylan Wilson
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 project adapts the AlphaGo paradigm for large language models (LLMs) by creating search trees of possible completions and evaluating them based on model confidence. The approach considers various paths in the search tree and scores them according to the model’s confidence in each completion, generating diverse and high-quality sequences. The paper aims to outline this paradigm and demonstrate its potential to improve LLMs in terms of output quality, error reduction, compound error elimination, diverse generation, iterative problem-solving, and self-training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This project uses a new way to generate text inspired by AlphaGo. It creates many possible endings and chooses the best one based on how confident the model is. This helps make the generated text more diverse, high-quality, and creative. The paper explains this approach and shows how it can be used in tasks like storytelling, content creation, machine translation, and automated summarization. |
Keywords
» Artificial intelligence » Self training » Summarization » Translation
