IDEAFix: An Evaluation Framework for Creative Defixation Prompting in LLMs

作者：Anonymous until acceptance · 发表于：Zenodo (CERN European Organization for Nuclear Research) · 年份：2026 · DOI：10.5281/zenodo.19838006 · 研究领域：Natural language processing、Computer science、Artificial intelligence、Linguistics、Psychology、Cognitive psychology

Dataset Card for Dataset Name This dataset accompanies the paper: "IDEAFix: An Evaluation Framework for Creative Defixation Prompting in LLMs," submitted to NeurIPS 2026. Dataset Details Dataset Description IDEAFix is a controlled evaluation dataset for studying divergent thinking and creative idea generation in Large Language Models (LLMs). It combines structured design briefs, attribute-based expansions, and method-inspired prompting strategies to enable systematic analysis of how task formulation and prompting influence LLM creativity. The dataset includes 81 design briefs expanded into 567 experimental conditions, paired with 25 structured prompts, yielding a total of 14,350 input prompts. Inference outputs from 6 LLMs are also provided. Main dataset files: Brief categories-Table 1.csv maps category symbols to their corresponding attributes. It contains three columns: Category (a single letter representing the category), Attributes (the category name), and Explanation (a short description of the category). Categories cover two main axes: adjective placement strategies within a brief (e.g., start, end, after the 1st word) and brief type classifications (e.g., product, service, strategy, process). A subset of categories is reserved for jailbreak-related briefs, flagging prompts that LLMs should not or may not be able to answer. Briefs-Table 1.csv contains 81 design briefs used in the benchmark, organized in a hierarchical structure. Each brief (e.g., Bookmark, Lemonade Stan...