Building structured synthetic datasets: The case of Blackbird Language Matrices (BLMs)
Authors: Paola Merlo, Giuseppe Samo, Vivi Nastase, Chunyang Jiang (University of Geneva, 2023)
Topic: Language Models

Abstract
Our goal is to investigate, and ultimately enhance, the degree to which existing large language models (LLMs) learn disentangled rule-based, compositional linguistic representations. We take the approach of developing curated synthetic data on a large scale with specific properties, using them to study sentence representations built with pretrained language models. Inspired by IQ tests, we create a new multiple-choice task. Solving this task requires a system capable of detecting complex linguistic patterns and paradigms in text representations. We present formal specifications of this task, illustrate it with two problems, and provide benchmarking results.

Introduction
Current consensus about large language models (LLMs), and neural networks (NNs) in general, is that to achieve better, possibly human-like abilities, we need to develop tasks and data that help us understand their current generalization capabilities and guide their training or tuning toward more complex and compositional skills. Humans are excellent generalizers. A large body of literature has demonstrated that the human mind is predisposed to generate rules from data and combine these rules in ways that are distinct from the activation patterns of neural networks. One possible approach to developing more robust methods is to encourage networks to learn disentangled decompositions of complex observations and the underlying regularities. To illustrate the complexity of rule decomposition, consider complex argument structure relations in the lexicon, such as the Spray/Load alternation in English. This alternation applies to verbs like spray, paint, spread, fill, stuff, and load, which describe covering surfaces or filling volumes. They occur in two subcategorization frames, related to each other in a regular way: the object of the preposition "with" is the subject of the onto frame, while the object of the onto prepositional phrase is the subject of the "with" frame. For example: 1. "John loaded the truck with hay." (AGENT LOCATIVE THEME) 2. "John loaded hay onto the truck." (AGENT THEME LOCATIVE) To learn the structure of such a complex alternation automatically, a neural network must identify the elements manipulated by the alternation, their relevant attributes, and the operations that manipulate these objects across multiple sentences. To study the factors that lead to learning more disentangled linguistic representations—those that reflect the underlying linguistic rules of grammar—we take the approach of developing curated synthetic data on a large scale, building diagnostic models from pretrained representations, and investigating the models' behavior. We introduce a new linguistic task inspired by the IQ test RPM (Raven 1938), which we call Blackbird Language Matrices (BLMs). BLMs define a prediction task to learn complex linguistic patterns and paradigms. In this paper, we provide precise formal specifications for the BLM task, illustrate it with two example problems, and present their benchmarking results. This demonstrates that the general formalism can be used to generate datasets with similar formats and specifications. Expanding the covered phenomena can be done systematically, enabling studies that combine or work across multiple phenomena and languages. We believe that this task brings us closer to investigating human linguistic intelligence.

Conclusion
In this paper, we have presented the new BLM task, provided its formal specifications, and illustrated the first instances of BLM problems and benchmarking results with baseline architectures. Current work is focused on developing new dedicated architectures based on Variational Autoencoders and creating new BLM problems. Future work involves further automating the data development pipeline to make the creation of BLM datasets accessible to linguists who may not be computationally oriented, as well as investigating the structure and nature of the information encoded in the learned inner representations.