研究发现：小型开放模型做自然语言推断，少量示例可取得与15个示例相近的成绩

This paper finds that a short, direct prompt with a few examples (In-Context Learning) lets small open LLMs match more complex prompting methods on natural language inference. The task, called NLI, asks whether one sentence follows from another, contradicts it, or is unrelated. Earlier LLM work on it leaned on complex prompting and sampling tricks, assuming more examples help. They tested 5 small open models, including Gemma 2 9B, Mistral 7B and Llama 3.1 8B, on 3 NLI datasets. Each got 1 short, direct prompt with 0 to 15 examples and no reasoning steps. Mistral 7B beat the 63.4% MNLI accuracy that a published in-context sampling method got with the same model. Gemma 2, Llama 3.1 and Mistral scored about the same with 1 example or 15 on MNLI and SciTail.