Large language models (LLMs) transfer well to new tasks out-of-the-box simply given a natural language prompt that demonstrates how to perform the task and no additional training. Prompting is a brittle process wherein small modifications to the prompt can cause large variations in the model predictions, and therefore significant effort is dedicated towards designing a painstakingly "perfect prompt" for a task. To mitigate the high degree of effort involved in prompt-design, we instead ask whether producing multiple effective, yet imperfect, prompts and aggregating them can lead to a high quality prompting strategy. Our observations motivate our proposed prompting method, ASK ME ANYTHING (AMA). We first develop an understanding of the effective prompt formats, finding that question-answering (QA) prompts, which encourage open-ended generation ("Who went to the park?") tend to outperform those that restrict the model outputs ("John went to the park. Output True or False."). Our approach recursively uses the LLM itself to transform task inputs to the effective QA format. We apply the collected prompts to obtain several noisy votes for the input's true label. We find that the prompts can have very different accuracies and complex dependencies and thus propose to use weak supervision, a procedure for combining the noisy predictions, to produce the final predictions for the inputs. We evaluate AMA across open-source model families (e.g., Neo, BLOOM, OPT, and T0) and model sizes (125M-175B parameters), demonstrating an average performance lift of 10.2% over the few-shot baseline. This simple strategy enables the open-source GPT-Neo-6B model to match and exceed the performance of few-shot GPT3-175B on 15 of 20 popular benchmarks. Averaged across these tasks, the GPT-Neo-6B model outperforms few-shot GPT3-175B. We release our code here: https://github.com/HazyResearch/ama_prompting
翻译:大型语言模型( LLM ) 向新任务转移 125 大型语言模型( LLM ), 只需以自然语言提示, 就能显示如何执行任务, 没有额外培训 。 提示是一个微小的过程, 微小修改快速可以导致模型预测出现巨大变化, 因此, 大量的努力致力于设计一个艰难的“ 完美快速” 任务 。 为了减轻快速设计所涉及的高度努力, 我们反问, 制作多个有效但不完善、 提示和汇总的多重任务是否会导致高质量的快速战略。 我们的观察激励了我们提议的快速方法 : ASK ME Anything (AMA ) 。 我们首先发展了对有效快速格式的理解, 发现解答( QAA) (QA) 的提示( ) 快速解答 (QA), 从而鼓励开放式的一代( “ 谁去公园吗? ” ) ) 。 我们的模型会反复使用少量的版本来将任务转换为有效的 QA 格式 。 我们用收集的快速的快速获得一些 快速的快速的票来整合输入, IM 和 IM IM IM 。