Relational Memory Augmented Language Models

Abstract: We present a memory-augmented approach to condition an autoregressive LLM on a knowledge graph. We represent the graph as a collection of relation triples and retrieve relevant relations for a given context to improve text generation. Experiments on WikiText-103, WMT19, and enwik8 English datasets demonstrate that our approach produces a better LLM in terms of perplexity and bits per character. We also show that relational memory improves coherence, is complementary to token-based memory, and enables causal interventions. Our model provides a simple yet effective way to combine an autoregressive LLM with a knowledge graph for a more coherent and logical generation.