Papers by Lingyun Song
Topology Imbalance and Relation Inauthenticity Aware Hierarchical Graph Attention Networks for Fake News Detection (2022.coling-1)
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| Challenge: | Existing methods to detect fake news focus on mining lexical and syntactic features. |
| Approach: | They propose a topology imbalance and Relation inauthenticity aware Hierarchical Graph Attention Networks to identify fake news on social media. |
| Outcome: | The proposed method outperforms state-of-the-art methods on real-world datasets. |
G-HiRel: Enhancing the Adaption to Knowledge Updating for Large Language Model Reasoning (2026.findings-acl)
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| Challenge: | Large language models (LLMs) have good performance in multiple reasoning tasks, but are limited to adapt the rapid knowledge updates in the real-world scenario. |
| Approach: | They propose an LLM reasoning framework with hierarchical relational retrieval for large-scale knowledge updating, named G-HiRel. |
| Outcome: | The proposed framework achieves superiority in terms of accuracy and interpretability on three benchmarks. |
A Robust Dual-debiasing VQA Model based on Counterfactual Causal Effect (2024.findings-emnlp)
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| Challenge: | Existing VQA models suffer from language bias that indicates a spurious correlation between textual questions and answers. |
| Approach: | They propose a model agnostic dual-debiasing framework that models two types of language bias by separate branches under counterfactual inference framework. |
| Outcome: | The proposed framework significantly reduces language bias and achieves state-of-the-art performance on the benchmark datasets. |