Posted by Cheng-Yu Hsieh, Student Researcher, and Chen-Yu Lee, Research Scientist, Cloud AI Team

Large language models (LLMs) have enabled a new data-efficient learning paradigm wherein they can be used to solve unseen new tasks via zero-shot or few-shot prompting. However, LLMs are challenging to deploy for real-world applications due to their sheer size. For instance, serving a single 175 billion LLM requires at least 350GB of GPU memory using specialized infrastructure, not to mention that today’s state-of-the-art LLMs are composed of over 500 billion parameters. Such computational requirements are inaccessible for many research teams, especially for applications that require low latency performance.

To circumvent these deployment challenges, practitioners often choose to deploy smaller specialized models instead. These smaller models are trained using one of two common paradigms: fine-tuning or distillation. Fine-tuning updates a pre-trained smaller model (e.g., BERT or T5) using downstream manually-annotated data. Distillation trains the same smaller models with labels generated by a larger LLM. Unfortunately, to achieve comparable performance to LLMs, fine-tuning methods require human-generated labels, which are expensive and tedious to obtain, while distillation requires large amounts of unlabeled data, which can also be hard to collect.

In “Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes”, presented at ACL2023, we set out to tackle this trade-off between model size and training data collection cost. We introduce distilling step-by-step, a new simple mechanism that allows us to train smaller task-specific models with much less training data than required by standard fine-tuning or distillation approaches that outperform few-shot prompted LLMs’ performance. We demonstrate that the distilling step-by-step mechanism enables a 770M parameter T5 model to outperform the few-shot prompted 540B PaLM model using only 80% of examples in a benchmark dataset, which demonstrates a more than 700x model size reduction with much less training data required by standard approaches.

While LLMs offer strong zero and few-shot performance, they are challenging to serve in practice. On the other hand, traditional ways of training small task-specific models require a large amount of training data. Distilling step-by-step provides a new paradigm that reduces both the deployed model size as well as the number of data required for training.

Distilling step-by-step

The key idea of distilling step-by-step is to extract informative natural language rationales (i.e., intermediate reasoning steps) from LLMs, which can in turn be used to train small models in a more data-efficient way. Specifically, natural language rationales explain the connections between the input questions and their corresponding outputs. For example, when asked, “Jesse’s room is 11 feet long and 15 feet wide. If she already has 16 square feet of carpet, how much more carpet does she need to cover the whole floor?”, an LLM can be prompted by the few-shot chain-of-thought (CoT) prompting technique to provide intermediate rationales, such as, “Area = length * width. Jesse’s room has 11 * 15 square feet.” That better explains the connection from the input to the final answer, “(11 * 15 ) – 16”. These rationales can contain relevant task knowledge, such as “Area = length * width”, that may originally require many data for small models to learn. We utilize these extracted rationales as additional, richer supervision to train small models, in addition to the standard task labels.

Overview on distilling step-by-step: First, we utilize CoT prompting to extract rationales from an LLM. We then use the generated rationales to train small task-specific models within a multi-task learning framework, where we prepend task prefixes to the input examples and train the model to output differently based on the given task prefix.

Distilling step-by-step consists of two main stages. In the first stage, we leverage few-shot CoT prompting to extract rationales from LLMs. Specifically, given a task, we prepare few-shot exemplars in the LLM input prompt where each example is composed of a triplet containing: (1)