Papers with semantic comprehension

3 papers
AELC: Adaptive Entity Linking with LLM-Driven Contextualization (2025.findings-emnlp)

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Challenge: Entity linking (EL) focuses on associating ambiguous mentions in text with corresponding entities in a knowledge graph.
Approach: Entity linking (EL) focuses on associating ambiguous mentions in text with corresponding entities in a knowledge graph.
Outcome: Experiments on four public benchmark datasets show that AELC achieves state-of-the-art performance.
Code Execution with Pre-trained Language Models (2023.findings-acl)

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Challenge: Pre-trained code intelligence models ignore the execution trace and only rely on source code and syntactic structures to understand code execution.
Approach: They develop a mutation-based data augmentation technique to create a Python dataset and task for code execution that challenges existing models.
Outcome: The proposed model outperforms existing models on code execution and shows its potential for zero-shot code-to-code search and text-to code generation.
HetGCoT: Heterogeneous Graph-Enhanced Chain-of-Thought LLM Reasoning for Academic Question Answering (2025.findings-emnlp)

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Challenge: graph neural networks capture structured graph information, but lack integration at the reasoning level.
Approach: They propose a framework that leverages graph structural information to reason interpretable academic QA results.
Outcome: The proposed framework outperforms sota baselines on OpenAlex and DBLP datasets.

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