ADAPTATION OF TEXT GENERATION STYLE TO A SPECIFIC AUDIENCE OR CONTENT

Abstract

Adaptation of text generation style to specific audiences or content can be achieved without costly fine-tuning. We freeze model weights and instead (i) search eight decoder hyperparameters with Bayesian optimization and (ii) prepend a one-line style cue that modulates readability. Experiments on five mathematical question-answering benchmarks (AQUA-RAT, MathQA, GSM8K, MAWPS, SVAMP) with three 8–14 B-parameter checkpoints (LLaMA-3.1-8B, DeepSeek-Qwen-8B/14B) show that 50-trial Optuna searches raise exact-match accuracy by up to 36 percentage points and close 5–10 points of the gap to 30–70 B fine-tuned baselines. The same settings transfer across tasks with under 2-point loss. Adding the child-friendly header leaves accuracy virtually unchanged while halving the Flesch–Kincaid grade level and shortening reasoning traces. All experiments fit within a few GPU-hours on a single A100, making the method practical for resource-constrained deployments. The study demonstrates that careful decoder control combined with micro-prompts delivers numerical correctness and audience-appropriate exposition without additional training or tuning time.

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