Papers by Sumin An

2 papers
ReTAG: Retrieval-Enhanced, Topic-Augmented Graph-Based Global Sensemaking (2025.findings-emnlp)

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Challenge: a prior graph-based approach to global sensemaking lacks retrieval mechanisms, topic specificity, and incurs high inference costs.
Approach: They propose a RetrievalEnhanced, Topic-Augmented Graph framework that retrieves relevant summaries from a topic.
Outcome: The proposed framework improves response quality while significantly reducing inference time compared to the baseline.
LCIRC: A Recurrent Compression Approach for Efficient Long-form Context and Query Dependent Modeling in LLMs (2025.naacl-long)

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Challenge: Large language models excel in generating coherent and contextually rich outputs, but their capacity to handle long-form contexts is limited by fixed-length position embeddings.
Approach: They propose a method that enables the efficient processing long-form sequences beyond the model’s length limit through recurrent compression without retraining the entire model.
Outcome: The proposed method significantly improves LLM’s ability to manage extended contexts, making it well-suited for tasks that require both comprehensive context understanding and query relevance.

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