When No Paths Lead to Rome: Benchmarking Systematic Neural Relational Reasoning
This addresses the need for better benchmarks in systematic relational reasoning for AI researchers, though it is incremental as it builds on prior work.
The authors tackled the problem of evaluating systematic relational reasoning in neural networks by introducing NoRA, a new benchmark that adds difficulty levels and requires models to go beyond path-based reasoning, as existing benchmarks are overly simplified.
Designing models that can learn to reason in a systematic way is an important and long-standing challenge. In recent years, a wide range of solutions have been proposed for the specific case of systematic relational reasoning, including Neuro-Symbolic approaches, variants of the Transformer architecture, and specialised Graph Neural Networks. However, existing benchmarks for systematic relational reasoning focus on an overly simplified setting, based on the assumption that reasoning can be reduced to composing relational paths. In fact, this assumption is hard-baked into the architecture of several recent models, leading to approaches that can perform well on existing benchmarks but are difficult to generalise to other settings. To support further progress in the field of systematic relational reasoning with neural networks, we introduce NoRA, a new benchmark which adds several levels of difficulty and requires models to go beyond path-based reasoning.