Shakespearean Sparks: The Dance of Hallucination and Creativity in LLMs' Decoding Layers

ACL ARR 2025 February Submission8277 Authors

Abstract: Large language models (LLMs) are known to hallucinate, a phenomenon often linked to creativity. While previous research has primarily explored this connection through theoretical or qualitative lenses, our work takes a quantitative approach to systematically examine the relationship between hallucination and creativity in LLMs. Given the philosophical nature of creativity, we propose a narrow definition tailored to LLMs and introduce an evaluation framework, HCL, which quantifies \textbf{H}allucination and \textbf{C}reativity across different \textbf{L}ayers of LLMs during decoding. Our empirical analysis reveals a tradeoff between hallucination and creativity that is consistent across layer depth, model type, and model size. Notably, across different model architectures, we identify a specific layer at each model size that optimally balances this tradeoff. Additionally, the optimal layer tends to appear in the early layers of larger models, and the confidence of the model is also significantly higher at this layer. These findings provide a quantitative perspective that offers new insights into the interplay between LLM creativity and hallucination.

Paper Type: Long

Research Area: Interpretability and Analysis of Models for NLP

Research Area Keywords: hierarchical & concept explanations

Contribution Types: Model analysis & interpretability, Data analysis

Languages Studied: English

Submission Number: 8277