Papers by Nicholas Popovič
Tracing Relational Knowledge Recall in Large Language Models (2026.findings-acl)
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| Challenge: | Feature attribution analyses of the trained probes reveal correlations between probe accuracy and relation specificity, entity connectedness, and how distributed the signal on which the probe relies is across attention heads. |
| Approach: | They evaluate latent representations derived from attention heads and MLP contributions . they show correlations between probe accuracy and relation specificity . |
| Outcome: | The proposed representations are compared with the representations obtained from attention heads and MLPs. |
Extractive Fact Decomposition for Interpretable Natural Language Inference in one Forward Pass (2025.emnlp-main)
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| Challenge: | Recent work in Natural Language Inference (NLI) uses atomic fact decomposition to enhance interpretability and robustness. |
| Approach: | They propose an encoder-only architecture that performs extractive atomic fact decomposition and interpretable inference without generative models. |
| Outcome: | The proposed architecture achieves competitive accuracy and improves robustness out of distribution and in adversarial settings over models based on extractive rationale supervision. |