Summary of Hierarchical Prompting Taxonomy: a Universal Evaluation Framework For Large Language Models Aligned with Human Cognitive Principles, by Devichand Budagam et al.
Hierarchical Prompting Taxonomy: A Universal Evaluation Framework for Large Language Models Aligned with Human Cognitive Principles
by Devichand Budagam, Ashutosh Kumar, Mahsa Khoshnoodi, Sankalp KJ, Vinija Jain, Aman Chadha
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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 proposed Hierarchical Prompting Taxonomy (HPT) assesses the capabilities of large language models (LLMs) by examining their cognitive demands on various tasks. The HPT utilizes a hierarchical framework, which structures five prompting strategies based on their cognitive requirements compared to human mental capabilities. This approach enables a comprehensive evaluation of an LLM’s problem-solving abilities and the complexity of datasets, offering a standardized metric for task complexity. Experimental results show that the Hierarchical Prompting Framework (HPF) enhances LLM performance by 2% to 63% compared to baseline performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are very smart computers that can perform many tasks. This paper introduces a new way to test these models, called Hierarchical Prompting Taxonomy or HPT. It helps us understand what tasks are too hard or too easy for the models and which ones they’re good at. The approach uses five different strategies to ask questions and solves problems, just like humans do. By using this method, we can see how well the models perform on different tasks and datasets. |
Keywords
» Artificial intelligence » Prompting
