Papers by ByeongJeong Kim
Probing-RAG: Self-Probing to Guide Language Models in Selective Document Retrieval (2025.findings-naacl)
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| Challenge: | Existing methods to optimize retrieve-and-generate processes for real-world scenarios may not be optimal for large language models. |
| Approach: | They propose a Probing-RAG which utilizes hidden state representations from the intermediate layers of language models to adaptively determine the necessity of additional retrievals for a given query. |
| Outcome: | The proposed method outperforms previous methods while reducing the number of redundant retrieval steps. |
FIZZ: Factual Inconsistency Detection by Zoom-in Summary and Zoom-out Document (2024.emnlp-main)
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| Challenge: | Existing methods for evaluating factual consistency in abstractive summarization systems have significant limitations, especially on refinement and interpretability. |
| Approach: | They propose a method for detecting summary factual inconsistency based on fine-grained atomic facts decomposition and adaptive granularity expansion. |
| Outcome: | The proposed method outperforms existing systems on the AGGREFACT benchmark dataset and achieves state-of-the-art performance. |