Complicating Prompts to Challenge AI Systems

The article discusses the evaluation and rating of the difficulty and complexity of a given question using various Large Language Models (LLMs). The authors present a prompt compression method to assess the efficiency of LLMs in handling complex questions. They propose a scoring system, where the overall score ranges from 1-10, with higher scores indicating greater difficulty and complexity. Additionally, they evaluate the number of reasoning steps required to answer the question.
The authors provide examples of prompts for various LLMs, including GPT-4 Compression, to demonstrate their approach. They highlight the limitations of uncontrollable token length in most cases, which can result in compression failures despite following the restrictions. The article also discusses previous work on solving quantitative reasoning problems using language models and the importance of constraint-aware pruning for efficient transformer inference.
To simplify the evaluation process, the authors propose a scoring system that considers both difficulty and reasoning steps. They encourage users to provide their overall score (on a scale of 1-10) and the number of reasoning steps required to answer the question. This approach allows users to quickly assess the complexity of a given question without requiring extensive knowledge of LLMs or quantitative reasoning.
The authors emphasize that their method demystifies complex concepts by using everyday language and engaging metaphors, making it more accessible to a broader audience. By providing concise summaries of the article’s key points, readers can quickly grasp the essence of the work without getting bogged down in technical details.
In summary, the article presents an effective method for evaluating the difficulty and complexity of questions using various LLMs. The proposed scoring system and prompt compression approach provide a straightforward way to assess the efficiency of these models in handling complex queries. By using simple language and engaging metaphors, the authors aim to make this evaluation process more accessible to a broader audience interested in understanding how well LLMs can handle difficult questions.