From Words to Widgets for Controllable LLM Generation
Natural language remains the predominant way people interact with large language models (LLMs). However, users often struggle to precisely express and control subjective preferences (e.g., tone, style, and emphasis) through prompting. We propose Malleable Prompting, a new interactive prompting technique for controllable LLM generation. It reifies preference expressions in natural language prompts into GUI widgets (e.g., sliders, dropdowns, and toggles) that users can directly configure to steer generation, while visualizing each control's influence on the output to support attribution and comparison across iterations. To enable this interaction, we introduce an LLM decoding algorithm that...
| Authors | Chao Zhang, Yiren Liu, Lunyiu Nie et al. |
|---|---|
| Primary category | cs.HC |
| Published | 13 April 2026 |
| Last updated at source | 10 August 2026 |
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