Study finds language style less crucial than format instructions for small LLMs

A recent test evaluated how eight local language models, ranging from 0.6B to 14B parameters, performed on 50 tasks across three language styles: plain, normal, and ornate. The results showed that for models of 4B parameters or fewer, normal language prompts yielded the best performance, with both simpler and more ornate phrasing causing a slight drop in accuracy. For larger models, the language style made little difference to the outcome. The key factor influencing accuracy was the presence of a clear, explicit instruction line specifying how the model should format its answer.