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This research introduces a novel context-based scoring mechanism derived from information theory to quantitatively assess the value and originality of large language model outputs. By integrating this score as a reward signal within reinforcement learning, the study successfully fine-tunes models to balance adherence with requests against fostering divergence in creative tasks like poetry generation. The work addresses a critical bottleneck in AI creativity by offering a method that improves output diversity without sacrificing accuracy or quality.
Thinking Outside the (Gray) Box: A Context-Based Score for Assessing Value and Originality in Neural Text Generation
arXiv:2502.13207v4 Announce Type: replace-cross Abstract: Despite the increasing use of large language models for creative tasks, their outputs often lack diversity. Common solutions, such as sampling at higher temperatures, can compromise the quality of the results. Dealing with this trade-off is still an open challenge in designing AI systems for creativity. Drawing on information theory, we propose a context-based score to quantitatively evaluate value and originality. This score incentivizes accuracy and adherence to the request while fostering divergence from the learned distribution. We show that our score can be used as a reward in a reinforcement learning framework to fine-tune large language models for maximum performance. We validate our strategy through experiments considering a variety of creative tasks, such as poetry generation and math problem solving, demonstrating that it enhances the value and originality of the generated solutions.
Abstract:Despite the increasing use of large language models for creative tasks, their outputs often lack diversity. Common solutions, such as sampling at higher temperatures, can compromise the quality of the results. Dealing with this trade-off is still an open challenge in designing AI systems for creativity. Drawing on information theory, we propose a context-based score to quantitatively evaluate value and originality. This score incentivizes accuracy and adherence to the request while fostering divergence from the learned distribution. We show that our score can be used as a reward in a reinforcement learning framework to fine-tune large language models for maximum performance. We validate our strategy through experiments considering a variety of creative tasks, such as poetry generation and math problem solving, demonstrating that it enhances the value and originality of the generated solutions.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG)
arXiv:2502.13207 [cs.CL]
(or arXiv:2502.13207v4 [cs.CL] for this version)
From: Giorgio Franceschelli Dr. [view email] [v1] Tue, 18 Feb 2025 19:00:01 UTC (256 KB) [v2] Mon, 4 Aug 2025 05:39:29 UTC (198 KB) [v3] Thu, 25 Sep 2025 09:33:30 UTC (214 KB) [v4] Mon, 17 Aug 2026 09:26:17 UTC (222 KB)
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