安裝中文字典英文字典辭典工具!
安裝中文字典英文字典辭典工具!
|
- Self-Refine: Iterative Refinement with Self-Feedback - a . . .
This new paradigm uses LLMs to execute tasks in natural language, where chain-of-thought, reflexion and retrieval are just patterns of usage and tools Initially named "prompting" but that doesn't do it justice Another alternative name was "language chains" to emphasise the compositional nature of LLM operations
- Self-Refine: Iterative Refinement with Self-Feedback
Self-Refine is a novel approach that allows LLMs to iteratively refine outputs and incorporate feedback along multiple dimensions to improve performance on diverse tasks Unlike prior work, it does not require supervised training data or reinforcement learning, and works with a single LLM
- SELF-REFINE — A New Milestone in the AI Era? | by Isaac . . .
This work introduced SELF-REFINE, which allows large language models to perform iterative refinement and self-assessment for improved output quality Operating within a single LLM, it requires
- Self-Refine Is An Iterative Refinement Loop For LLMs
The Self-Refine study demonstrates how an LLM can provide iterative self-refinement without additional training, yielding higher-quality outputs on a wide range of tasks
- Self-Refine: Iterative Refinement with Self-Feedback - GitHub
With Self-Refine, LLMs can generate feedback on their work, use it to improve the output, and repeat this process Table of Contents Nov 2023: Added visual self-refine examples and colabs Use GPT4-V to write tikz code for diagrams, and improve them iteratively Stokes' Theorem Example Unicorn Example We use prompt-lib for querying LLMs
- Self-Refine: Iterative Refinement with Self-Feedback - OpenReview
TL;DR: Self-Refine is a novel approach that allows LLMs to iteratively refine outputs and incorporate feedback along multiple dimensions to improve performance on diverse tasks Like humans, large language models (LLMs) do not always generate the best output on their first try
- SELF-REFINE Iterative Refinement with Self-Feedback - arXiv. org
We present SELF-REFINE: an iterative self-refinement algorithm that alternates between two gener-ative steps–FEEDBACK and REFINE These steps work in tandem to generate high-quality outputs Given an initial output generated by a model M, we pass it back to the same model M to get feedback
|
|
|