Summary of Lampo: Large Language Models As Preference Machines For Few-shot Ordinal Classification, by Zhen Qin and Junru Wu and Jiaming Shen and Tianqi Liu and Xuanhui Wang
LAMPO: Large Language Models as Preference Machines for Few-shot Ordinal Classification
by Zhen Qin, Junru Wu, Jiaming Shen, Tianqi Liu, Xuanhui Wang
First submitted to arxiv on: 6 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)
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 paper introduces LAMPO, a novel framework that leverages Large Language Models (LLMs) for solving few-shot multi-class ordinal classification tasks. Unlike conventional methods, LAMPO uses the LLM as a preference machine to make relative comparative decisions between the test instance and each demonstration. The framework addresses limitations in previous methods, including context length constraints, ordering biases, and challenges associated with absolute point-wise estimation. Experimental results on seven public datasets demonstrate LAMPO’s competitive performance across various applications, such as movie review analysis and hate speech detection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LAMPO is a new way to use big language models for a specific type of problem-solving. Normally, these models are asked to make predictions about individual things, like whether something is good or bad. But LAMPO lets the model compare things to each other instead. This makes it better at solving problems that involve comparing things in order, like ranking movies or detecting hate speech. |
Keywords
» Artificial intelligence » Classification » Context length » Few shot
