Controlling the Diversity-Quality Trade-Off of LLMs by Merging Models Before and After Alignment
Alignment is known to change the output characteristics of large language models (LLMs). Base models tend to generate more diverse outputs, whereas aligned models tend to produce higher-quality outputs. The appropriate balance between the two depends on the task. Existing methods either require costly fine-tuning or, like temperature sampling, mainly improve lexical diversity rather than semantic diversity. We introduce a depth-aware FrankenMerging approach for controlling the diversity--quality trade-off between an aligned model and its base model without additional training. This approach combines the lower layers of an aligned model with the higher layers of its base model based on the...
| Authors | Yasuaki Sumita, Tomoharu Iwata, Toshiyuki Tanaka |
|---|---|
| Primary category | cs.CL |
| Published | 16 October 2026 |
| Last updated at source | 16 October 2026 |
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