Large language models (LLMs) are impressive, but they can be a bit of a loose cannon. They often write long, rambling responses when you really just want a short, focused answer. Wouldn’t it be great if you could tell an LLM exactly what kind of response you’re looking for—short and sweet, detailed and complex, or somewhere in between? New research explores how to fine-tune LLMs so you can control their output’s linguistic complexity. Researchers have developed a technique called ‘multi-control tuning’ (MCTune), which allows you to tweak specific linguistic features of an LLM's output. Think of it like giving the LLM a set of dials to adjust the number of nouns, verbs, adjectives, sentence length, reading ease, and more. By adding these linguistic controls during the fine-tuning process, the LLM learns to tailor its responses to specific requirements. This is a significant step towards making LLMs more predictable and user-friendly. It means we can move beyond simply prompting an LLM and hoping for the best. Instead, we can start requesting outputs that are customized to our exact needs, whether we’re looking for concise summaries, engaging stories, or just the right tone. The researchers experimented with fine-tuning LLaMA 2 7B on datasets like Alpaca-GPT4 and WizardLM. The results show that MCTune not only improves controllability but also boosts the overall quality of the responses. This suggests that linguistic constraints can actually help LLMs learn more effectively. This research is exciting because it suggests we can train LLMs to be more responsive to different linguistic preferences. Imagine having a slider to make text more accessible for different audiences. Or, imagine generating content perfectly tailored to a particular style guide. While the technology is still under development, the initial results of this research suggest that it may not be long before we have even more control over the power of large language models.