Papers by Luyang Lin
IndiVec: An Exploration of Leveraging Large Language Models for Media Bias Detection with Fine-Grained Bias Indicators (2024.findings-eacl)
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| Challenge: | Existing studies on social media bias detection focus on fine-tuning models specific to particular datasets and testing them on corresponding test sets. |
| Approach: | They propose a general bias detection framework, IndiVec, built upon large language models and vector databases. |
| Outcome: | The proposed framework outperforms baseline methods on four political bias datasets and provides explicit top-k indicators to interpret bias predictions. |
Investigating Bias in LLM-Based Bias Detection: Disparities between LLMs and Human Perception (2025.coling-main)
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| Challenge: | Detecting media bias is critical due to the spread of misinformation and disinformation on social media platforms. |
| Approach: | They investigate the presence and nature of bias within large language models and its consequential impact on media bias detection. |
| Outcome: | The proposed debiasing strategies include prompt engineering and model fine-tuning. |
LLMEdgeRefine: Enhancing Text Clustering with LLM-Based Boundary Point Refinement (2024.emnlp-main)
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| Challenge: | LLMEdgeRefine is an iterative clustering method enhanced by large language models . existing clustering methods struggle with domain-specific fine-tuning and outliers . |
| Approach: | They propose an iterative clustering method enhanced by large language models focusing on edge points refinement . authors propose to use LLMs to iterate clusters and iterating to improve semantic coherence . |
| Outcome: | The proposed method outperforms state-of-the-art methods and offers robustness, adaptability, and cost-efficiency for diverse text clustering applications. |