Summary of Interpretable Cross-examination Technique (ice-t): Using Highly Informative Features to Boost Llm Performance, by Goran Muric et al.
Interpretable Cross-Examination Technique (ICE-T): Using highly informative features to boost LLM performance
by Goran Muric, Ben Delay, Steven Minton
First submitted to arxiv on: 8 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 The Interpretable Cross-Examination Technique (ICE-T) is a novel approach that leverages structured multi-prompt techniques with Large Language Models (LLMs) to improve classification performance. ICE-T addresses the limitations of traditional models by generating prompts from multiple directions, allowing an LLM to approach a problem in different ways. The responses are then converted into numerical feature vectors and processed by a traditional classifier. This method maintains high interpretability while enabling smaller models to achieve or exceed the performance of larger models under zero-shot conditions. ICE-T is demonstrated across various data sources, including medical records and legal documents, consistently surpassing the zero-shot baseline in terms of F1 scores. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ICE-T is a new way for computers to understand things without being taught everything at once. It works by giving large language models many different ways to think about a problem, and then using those thoughts to make decisions. This helps computers be better at making decisions and understanding why they made them. ICE-T is important because it lets smaller computers do the same job as bigger ones, but in a way that’s easier to understand. |
Keywords
» Artificial intelligence » Classification » Prompt » Zero shot
