BILLY: Steering Large Language Models via Merging Persona Vectors for Creative Generation (2026.eacl-long)
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| Challenge: | Multi-LLM systems enhance creativity of large language models by simulating human collective intelligence but suffer from significant drawbacks, such as high computational costs and inference latency. |
| Approach: | They propose a training-free framework that captures the benefits of multi-LLM collaboration by extracting and blending multiple distinct persona vectors directly in the model’s activation space. |
| Outcome: | The proposed framework surpasses model prompting and traditional multi-LLM approaches while significantly reducing inference time and computational costs. |
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