Papers by Linqing Chen
Breaking the Corpus Bottleneck for Context-Aware Neural Machine Translation with Cross-Task Pre-training (2021.acl-long)
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| Challenge: | Context-aware neural machine translation (NMT) remains challenging due to the lack of large-scale document-level parallel corpora. |
| Approach: | They propose to use large-scale parallel datasets and source-side monolingual documents to improve context-aware neural machine translation. |
| Outcome: | The proposed model can be used to translate both sentences and documents on four translation tasks. |
Streamlining Biomedical Research with Specialized LLMs (2025.coling-demos)
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| Challenge: | Using large language models, we can generate accurate, context-aware responses with minimal prompts. |
| Approach: | They propose a system that integrates domain-specific large language models with advanced information retrieval techniques to deliver comprehensive and context-aware responses. |
| Outcome: | The proposed system improves quality of dialogue generation and improves efficiency in the biomedical and pharmaceutical domains. |
Multimodal Chemical Structure-Text Coreference in Intellectual Property via Rule-guided Reinforcement Learning (2026.findings-acl)
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| Challenge: | Existing tools for identifying chemical structures and textual referents are inadequate for this multimodal task. |
| Approach: | They propose a RULE-guided multimodal Reinforcement learning framework for chemical structure-text coreference . RULER is a rule-driven reinforcement learning framework that uses rule-based reward functions to obtain the correct domain knowledge. |
| Outcome: | The proposed framework improves on the baseline framework and shows superior efficacy. |
CRAB: A Benchmark for Evaluating Curation of Retrieval-Augmented LLMs in Biomedicine (2025.emnlp-industry)
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| Challenge: | Recent development in Retrieval-Augmented Large Language Models (LLMs) have shown great promise in biomedical applications. |
| Approach: | They propose a multilingual benchmark to evaluate retrieval-augmented large language models' curation ability. |
| Outcome: | The proposed benchmark is available in English, French, German and Chinese. |
The Dominance of Text Space: Unveiling the Asymmetric Nature of Cross-Modal Alignment in Large Language Models (2026.acl-long)
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| Challenge: | Existing methods for cross-modal alignment assume a symmetric interaction between visual and textual modalities, implying that both spaces adapt to each other. |
| Approach: | They propose a method that regularizes the projector to maintain the geometric structure of the text embedding space via spectral filtering. |
| Outcome: | The proposed method preserves the LLM’s inherent linguistic capabilities and reduces object hallucination significantly better than standard fine-tuning methods. |