Papers by Songlin Zhai
Can Large Language Models Understand DL-Lite Ontologies? An Empirical Study (2024.findings-emnlp)
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| Challenge: | Large language models (LLMs) have shown remarkable proficiency in understanding textual data and revolutionizing the field of natural language processing. |
| Approach: | They empirically analyze LLMs' capability of understanding Description Logic (DL) ontologies covering 6 representative tasks from syntactic and semantic aspects. |
| Outcome: | The proposed model can understand formal syntax and model-theoretic semantics of concepts and roles, but struggle with understanding TBox NI transitivity and handling ontologies with large ABoxes. |
TEF: Causality-Aware Taxonomy Expansion via Front-Door Criterion (2025.coling-main)
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| Challenge: | Existing research still faces spurious query-anchor matching due to unobserved factors. |
| Approach: | They propose a model that uses the front-door criteria to decompose the expansion process into a parser module and a connector to isolate confounding effects. |
| Outcome: | Extensive experiments on three benchmarks validate the effectiveness of the proposed model. |
Parameter-Aware Contrastive Knowledge Editing: Tracing and Rectifying based on Critical Transmission Paths (2025.acl-long)
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| Challenge: | Large language models (LLMs) encode vast amounts of knowledge in their parameters, but the acquired knowledge can be incorrect or outdated over time, necessitating rectification after pre-training. |
| Approach: | They propose a method that captures key information flows that influence model predictions . they propose 'critical transmission paths' to improve model editing . |
| Outcome: | The proposed method improves on two prominent datasets and three widely used LLMs. |