Title: A Fictional Q&A Dataset for Studying Memorization and Knowledge Acquisition
Primary area: foundation or frontier models, including LLMs
Keywords: large language models, memorization, knowledge acquisition, datasets

Abstract: When language models are trained on textual data, they acquire both knowledge about the structure of language as well as knowledge of facts about the world.
At inference time, their knowledge of facts can be leveraged to solve interesting problems and perform useful knowledge work for users.
It is well known that language models can verbatim memorize long sequences from their training data.
However, it is much less well understood how language models memorize facts seen during training.
In this work, we propose a new dataset to specifically empower researchers to study the dual processes of fact memorization and verbatim sequence memorization.
The dataset consists of synthetically-generated, webtext-like documents about fictional events, as well as question-answer pairs about the events.
We conduct training experiments showing how synthetic data about fictional events can be effective in teasing apart different forms of memorization.
We also document the challenges in effectively building realistic, fictional synthetic data.

TL;DR: We develop a synthetic dataset that enables us to study memorization and generalization of factual knowledge in LLMs.