A Codespace Autoencoder for Language Tasks

Celine Lee, Md Arafat Sultan, Tahira Naseem, Alexander M Rush, Ramón Fernandez Astudillo

Keywords: LLM, autoencoders, code generation, data generation

TL;DR: We propose a autoencoder that models observed text data as being generated from underlying code with a dataset level function library.

Abstract:

Modern language modeling datasets require models to handle compositional reasoning, fact recall, and task-specific constraints. While these tasks are expressed in natural language, they often imply an underlying symbolic representation. In this work, we consider methods for extracting a latent symbolic representation in an unsupervised manner. We propose an autoencoder that models observed text data as being generated from underlying code with a dataset level function library. Our method is non-parametric and leverages in-context learning and code interpretation for inference. Code as the latent symbolic representation offers two key advantages. First, code offers a structured space that can be explored via modular functions; second, code is interpretably executable using deterministic and neural interpreters, enabling compositional and programmatic decoding into text. By identifying and composing patterns in this latent space, we can sample programs that produce correct, diverse, and task-relevant text through program execution.
We demonstrate how our method induces a latent space with modern LLMs, explore patterns discovered within it, and evaluate text data synthesized from our induced latent space.

Primary Area: foundation or frontier models, including LLMs

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Submission Number: 12941