Interpreting Language Models Through Knowledge Graph Extraction

Language models acquire knowledge unevenly during training: The paper demonstrates that BERT-based models don't learn all types of knowledge at the same rate. By tracking knowledge acquisition across training stages, researchers found that models develop certain linguistic capabilities before others, suggesting that understanding when and how models learn different concepts is crucial for predicting their behavior on real-world tasks.

Current accuracy metrics alone are insufficient for model evaluation: Traditional performance measurements don't capture what knowledge a model has actually learned or how it reasons. This work proposes using knowledge graph extraction as an alternative evaluation framework, highlighting that practitioners need multiple diagnostic tools beyond standard benchmarks to truly understand model capabilities and limitations.

Different model variants have distinct linguistic strengths and weaknesses: The comparison of DistilBERT, BERT-base, and RoBERTa revealed that model variations excel at different linguistic tasks. This finding is important for responsible AI deployment—organizations should match model selection to their specific use case rather than assuming larger or more popular models are universally better.

Interpretability methods can help identify and fix model gaps: By using part-of-speech analysis and graph-based metrics, the paper shows that practitioners can diagnose why a model might fail on certain tasks and identify targeted datasets to improve performance. This diagnostic capability supports more transparent and accountable AI development.

Probing tasks offer a practical window into model knowledge: Using simple "fill-in-the-blank" queries to extract structured knowledge provides an accessible method for non-specialists to investigate what language models have learned, making model behavior more transparent and auditable.