Hierarchical Prompting Taxonomy: A Universal Evaluation Framework for Large Language Models Aligned with Human Cognitive Principles

• URL: http://arxiv.org/abs/2406.12644v4
• Date: Thu, 12 Dec 2024 02:37:52 GMT
• Title: Hierarchical Prompting Taxonomy: A Universal Evaluation Framework for Large Language Models Aligned with Human Cognitive Principles
• Authors: Devichand Budagam, Ashutosh Kumar, Mahsa Khoshnoodi, Sankalp KJ, Vinija Jain, Aman Chadha,
• Abstract summary: This paper presents Hierarchical Prompting Taxonomy (HPT), grounded on human cognitive principles. It is designed to assess large language models (LLMs) by examining the cognitive demands of various tasks. It assesses the complexity of tasks with the Hierarchical Prompting Index (HPI), which demonstrates the cognitive competencies of LLMs.
• Score: 1.8853906324722882
• License:
• Abstract: 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.

Related papers

• A Survey on Large Language Models with some Insights on their Capabilities and Limitations [0.3222802562733786]
  Large Language Models (LLMs) exhibit remarkable performance across various language-related tasks. LLMs have demonstrated emergent abilities extending beyond their core functions. This paper explores the foundational components, scaling mechanisms, and architectural strategies that drive these capabilities.
  arXiv  Detail & Related papers  (2025-01-03T21:04:49Z)
• Probing Ranking LLMs: Mechanistic Interpretability in Information Retrieval [22.875174888476295]
  We study the workings of state-of-the-art, fine-tuning-based passage-reranking transformer networks. Our approach involves a probing-based, layer-by-layer analysis of neurons within ranking LLMs. We identify individual or groups of known human-engineered and semantic features within the network's activations.
  arXiv  Detail & Related papers  (2024-10-24T08:20:10Z)
• Beyond Binary: Towards Fine-Grained LLM-Generated Text Detection via Role Recognition and Involvement Measurement [51.601916604301685]
  Large language models (LLMs) generate content that can undermine trust in online discourse. Current methods often focus on binary classification, failing to address the complexities of real-world scenarios like human-LLM collaboration. To move beyond binary classification and address these challenges, we propose a new paradigm for detecting LLM-generated content.
  arXiv  Detail & Related papers  (2024-10-18T08:14:10Z)
• 60 Data Points are Sufficient to Fine-Tune LLMs for Question-Answering [50.12622877002846]
  Large language models (LLMs) encode extensive world knowledge through pre-training on massive datasets, which can be fine-tuned for the question-answering (QA) task. We categorize supervised fine-tuning (SFT) data based on the extent of knowledge memorized by the pretrained LLMs. Our experiments show that as few as 60 data points during the SFT stage can activate the knowledge encoded during pre-training, enabling LLMs to perform the QA task.
  arXiv  Detail & Related papers  (2024-09-24T07:38:38Z)
• Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More? [54.667202878390526]
  Long-context language models (LCLMs) have the potential to revolutionize our approach to tasks traditionally reliant on external tools like retrieval systems or databases. We introduce LOFT, a benchmark of real-world tasks requiring context up to millions of tokens designed to evaluate LCLMs' performance on in-context retrieval and reasoning. Our findings reveal LCLMs' surprising ability to rival state-of-the-art retrieval and RAG systems, despite never having been explicitly trained for these tasks.
  arXiv  Detail & Related papers  (2024-06-19T00:28:58Z)
• Meta Reasoning for Large Language Models [58.87183757029041]
  We introduce Meta-Reasoning Prompting (MRP), a novel and efficient system prompting method for large language models (LLMs) MRP guides LLMs to dynamically select and apply different reasoning methods based on the specific requirements of each task. We evaluate the effectiveness of MRP through comprehensive benchmarks.
  arXiv  Detail & Related papers  (2024-06-17T16:14:11Z)
• Evaluating LLMs' Mathematical Reasoning in Financial Document Question Answering [53.56653281752486]
  This study explores Large Language Models' mathematical reasoning on four financial question-answering datasets. We focus on sensitivity to table complexity and performance variations with an increasing number of arithmetic reasoning steps. We introduce a novel prompting technique tailored to semi-structured documents, matching or outperforming other baselines in performance.
  arXiv  Detail & Related papers  (2024-02-17T05:10:18Z)
• On Evaluating the Integration of Reasoning and Action in LLM Agents with Database Question Answering [25.57202500348071]
  This study introduces a new long-form database question answering dataset designed to evaluate how Large Language Models interact with a database. The task requires LLMs to strategically generate multiplesql queries to retrieve sufficient data from a database, to reason with the acquired context, and to synthesize them into a comprehensive analytical narrative. We propose and evaluate two interaction strategies, and provide a fine-grained analysis of the individual stages within the interaction.
  arXiv  Detail & Related papers  (2023-11-16T09:55:07Z)
• Improving Open Information Extraction with Large Language Models: A Study on Demonstration Uncertainty [52.72790059506241]
  Open Information Extraction (OIE) task aims at extracting structured facts from unstructured text. Despite the potential of large language models (LLMs) like ChatGPT as a general task solver, they lag behind state-of-the-art (supervised) methods in OIE tasks.
  arXiv  Detail & Related papers  (2023-09-07T01:35:24Z)
• Metacognitive Prompting Improves Understanding in Large Language Models [12.112914393948415]
  We introduce Metacognitive Prompting (MP), a strategy inspired by human introspective reasoning processes. We conduct experiments on four prevalent Large Language Models (LLMs) across ten natural language understanding (NLU) datasets. MP consistently outperforms existing prompting methods in both general and domain-specific NLU tasks.
  arXiv  Detail & Related papers  (2023-08-10T05:10:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.