Papers › Hierarchical Prompting Taxonomy: A Universal Evaluation Framework for Large Language...
Hierarchical Prompting Taxonomy: A Universal Evaluation Framework for Large Language Models Aligned with Human Cognitive Principles
Devichand Budagam, Ashutosh Kumar, Mahsa Khoshnoodi, Sankalp KJ, Vinija Jain, Aman Chadha
Assessing the effectiveness of large language models (LLMs) in performing different tasks is crucial for understanding their strengths and weaknesses. This paper presents Hierarchical Prompting Taxonomy (HPT), grounded on human cognitive principles and designed to assess LLMs by examining the cognitive demands of various tasks. The HPT utilizes the Hierarchical Prompting Framework (HPF), which structures five unique prompting strategies in a hierarchical order based on their cognitive requirement on LLMs when compared to human mental capabilities. It assesses the complexity of tasks with the Hierarchical Prompting Index (HPI), which demonstrates the cognitive competencies of LLMs across diverse datasets and offers insights into the cognitive demands that datasets place on different LLMs. This approach enables a comprehensive evaluation of an LLMs problem solving abilities and the intricacy of a dataset, offering a standardized metric for task complexity. Extensive experiments with multiple datasets and LLMs show that HPF enhances LLM performance by 2% to 63% compared to baseline performance, with GSM8k being the most cognitively complex task among reasoning and coding tasks with an average HPI of 3.20 confirming the effectiveness of HPT. To support future research and reproducibility in this domain, the implementations of HPT and HPF are available here.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Arithmetic Reasoning | GSM8K | Claude 3.5 Sonnet (HPT) | Accuracy | 97.72 | #1 of 164 | Archive leaderboard | report |
| Common Sense Reasoning | CommonsenseQA | GPT-4o (HPT) | Accuracy | 92.54 | #1 of 38 | Archive leaderboard | report |
| Machine Translation | IWSLT 2017 | GPT-4o (HPT) | BLEU score | 32 | #1 of 1 | Archive leaderboard | report |
| Question Answering | BoolQ | Mistral-Nemo 12B (HPT) | Accuracy | 99.87 | #1 of 65 | Archive leaderboard | report |
| Text Summarization | SAMSum | GPT-4o (HPT) | ROUGE-L | 30 | #12 of 12 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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