What to do when few-shot learning isn’t enough…

Cameron R. Wolfe, Ph.D.

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Towards Data Science

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17 min read

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Aug 7

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The popularization of large language models (LLMs) has completely shifted how we solve problems as humans. In prior years, solving any task (e.g., reformatting a document or classifying a sentence) with a computer would require a program (i.e., a set of commands precisely written according to some programming language) to be created. With LLMs, solving such problems requires no more than a textual prompt. For example, we can prompt an LLM to reformat any document via a prompt similar to the one shown below.

As demonstrated in the example above, the generic text-to-text format of LLMs makes it easy for us to solve a wide variety of problems. We first saw a glimpse of this potential with the proposal of GPT-3 [18], showing that sufficiently-large language models can use few-shot learning to solve many tasks with surprising accuracy. However, as the research surrounding LLMs progressed, we began to move beyond these basic (but still very effective!) prompting techniques like zero/few-shot learning.

Instruction-following LLMs (e.g., InstructGPT and ChatGPT) led us to explore whether language models could solve truly difficult tasks. Namely, we wanted to use LLMs for more than just toy problems. To be practically useful, LLMs need to be capable of following complex instructions and performing multi-step reasoning to correctly answer difficult questions posed by a human. Unfortunately, such problems are often not solvable using basic prompting techniques. To eliciting complex problem-solving behavior from LLMs, we need something more sophisticated.

Expanding the scope of what’s possible…

In a prior post, we learned about more fundamental methods of prompting for LLMs, such as…