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cs.CL2026
Breaking Homogeneity: Diversifying Persona Sets for Creative LLM Outputs
Sang Bin Moon, Nicole Cho, Daniel Borrajo +2
Language models often produce homogeneous responses to open-ended tasks; such homogeneity can spawn groupthink-the convergence of ideas toward a singular and potentially suboptimal…
cs.LG2026
Entropy-informed Decoding: Adaptive Information-Driven Branching
Benjamin Patrick Evans, Sumitra Ganesh, Leo Ardon
Large language models (LLMs) achieve remarkable generative performance, yet their output quality is dependent on the decoding strategy. While sampling-based methods (e.g., top-k, n…