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CREATE:連想的創造性をLLMで測る新たな試み
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- 本研究では、LLMの創造的な連想推論能力を評価するベンチマーク「CREATE」を導入し、概念間の新規かつ意味のある関連性を評価します。
- CREATEは、モデルの持つ知識から概念を結びつける多様な経路を生成させ、経路の特異性と多様性を評価することで、創造性を客観的に評価します。
- 最先端モデルの評価により、優れたモデルは高い創造性を示しましたが、思考モデルが常に効果的とは限らず、創造的なプロンプトも限定的な改善にとどまりました。
Abstract
A key component of creativity is associative reasoning: the ability to draw novel yet meaningful connections between concepts. We introduce CREATE, a benchmark designed to evaluate models' capacity for creative associative reasoning. CREATE requires models to generate sets of paths connecting concepts in a model's parametric knowledge. Paths should have high specificity (distinctiveness and closeness of the concept connection) and high diversity (dissimilarity from other paths), and models are scored more highly if they produce a larger set of strong, diverse paths. This task shares demands of real creativity tasks like hypothesis generation, including an extremely large search space, but enables collection of a sizable benchmark with objective answer grading. Evaluation of frontier models shows that the strongest models achieve higher creative utility than others, with the high multiplicity of answers and complexity of the search making benchmark saturation difficult to achieve. Furthermore, our results illustrate that thinking models are not always more effective on our task, even with high token budgets. Recent approaches for creative prompting give some but limited additional improvement. CREATE provides a sandbox for developing new methods to improve models' capacity for associative creativity.
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