arXiv:2511.05650v2 Announce Type: replace Abstract: Alignment has greatly improved large language models (LLMs)' output quality at the cost of diversity, yielding highly similar outputs across generations, especially in open-ended generation tasks. We propose Base-Aligned Model Collaboration (BACo), an inference-time token-level model collaboration framework that dynamically combines a base LLM with its aligned counterpart to optimize diversity and quality. Using uncertainty and content-based signals, BACo employs routing strategies to determine, at each token, which model to decode from. Prio
The increasing prevalence of aligned LLMs has highlighted a trade-off between output quality and diversity, prompting research into methods to mitigate this issue without sacrificing alignment benefits.
Improving diversity in LLM outputs while maintaining quality is crucial for applications requiring creativity, nuanced responses, and avoiding homogeneous content generation.
The ability to dynamically combine base and aligned LLMs during inference could lead to more versatile and less predictable AI outputs, expanding their utility in various fields.
- · AI developers
- · Content creators
- · Generative AI platforms
- · Monolithic LLM approaches
Increased sophistication of generative AI models, offering a better balance between specificity and creativity.
Broader adoption of AI in fields requiring diverse and novel outputs, such as design, art, and complex problem-solving.
Potential for new forms of AI-human collaboration where AI acts as a more diverse ideation partner rather than a predictable generator.
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Read at arXiv cs.CL