LLMs Show Promise in Programming by Example, But Struggle with New Problem Types

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This is a Plain English Papers summary of a research paper called LLMs Show Promise in Programming by Example, But Struggle with New Problem Types. If you like these kinds of analysis, you should join AImodels.fyi or follow us on Twitter.

Overview

This paper investigates the ability of Large Language Models (LLMs) to solve Programming-by-Examples (PBE) tasks.
PBE aims to generate algorithms from input-output examples, which is important both practically and theoretically.
The researchers experiment on classic domains like lists and strings, as well as an uncommon graphics programming domain.
They find that while pretrained LLMs are not effective at PBE, they can be fine-tuned for much higher performance, as long as the test problems are in-distribution.
The paper analyzes what causes these models to succeed and fail, and explores ways to achieve better out-of-distribution generalization.

Plain English Explanation

Programming-by-Examples (PBE) is a way to create computer programs by showing the program examples of what you want it to do, rather than writing out the code step-by-step. This can be very useful for [end-users](https://aimodels.fyi/papers/arxiv/evaluation-programming-skills-l...

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