Papers by Chin-teng Lin
Context-attended Adversarial Reinforcement Learning for Robust Multi-step Retrieval Augmented Generation (2026.findings-acl)
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| Challenge: | Existing approaches to multi-step retrieval-augmented generation are susceptible to retrieval noise and fabricated documents in real-world scenarios. |
| Approach: | They propose a framework for multi-step retrieval-augmented generation that incorporates external knowledge into a retriever to generate responses from adversarial samples. |
| Outcome: | The proposed framework improves performance in multiple noisy scenarios and can be used to improve multi-step retrieval-augmented generation. |
Transforming Brainwaves into Language: EEG Microstates Meet Text Embedding Models for Dementia Detection (2025.acl-srw)
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| Challenge: | Dementia is recognised as the seventh leading cause of mortality globally and plays a major role in increasing disability and dependence among older adults. |
| Approach: | They propose to represent electroencephalography microstates as symbolic, language-like sequences and use text embedding and time-series deep learning models for classification. |
| Outcome: | The proposed method achieves a high accuracy of 94.31% on 1001 EEG data from multiple countries and eliminates fixed configurations and costly/invasive modalities. |