Papers by Boyoung Kim
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. |
ReSCORE: Label-free Iterative Retriever Training for Multi-hop Question Answering with Relevance-Consistency Supervision (2025.acl-long)
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| Challenge: | Multi-hop question answering requires reasoning across multiple documents to answer complex questions. |
| Approach: | They propose a method for training dense retrievers for multi-hop question answering . they leverage large language models to measure document-question relevance with answer consistency . their results lead to state-of-the-art Exact Match and F1 scores for MHQA . |
| Outcome: | Evaluated on three MHQA benchmarks, the proposed method improves retrieval performance . it leads to state-of-the-art Exact Match and F1 scores for the proposed technique . |