Papers by Jeongyeon Park
Improving Zero-shot Reader by Reducing Distractions from Irrelevant Documents in Open-Domain Question Answering (2023.findings-emnlp)
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| Challenge: | Large language models (LLMs) enable zero-shot approaches in open domain question answering (ODQA), yet with limited advancements as the reader is compared to the retriever. |
| Approach: | They propose to use a distraction-aware answer selection framework to mitigate the impact of irrelevant documents in the retrieved set and the overconfidence of the generated answers to enhance the performance of zero-shot readers. |
| Outcome: | The proposed approach handles distraction across diverse scenarios, enhancing the performance of zero-shot readers. |
Lossless Acceleration of Large Language Models with Hierarchical Drafting based on Temporal Locality in Speculative Decoding (2025.findings-naacl)
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Sukmin Cho, Sangjin Choi, Taeho Hwang, Jeongyeon Seo, Soyeong Jeong, Huije Lee, Hoyun Song, Jong C. Park, Youngjin Kwon
| Challenge: | Existing methods for drafting and verifying tokens require significant fine-tuning or have inconsistent performance across tasks. |
| Approach: | They propose a lossless drafting approach that organizes various token sources into multiple databases in a hierarchical framework based on temporal locality. |
| Outcome: | The proposed method outperforms existing database drafting methods on Spec-Bench using 7B and 13B parameters. |
Typos that Broke the RAG’s Back: Genetic Attack on RAG Pipeline by Simulating Documents in the Wild via Low-level Perturbations (2024.findings-emnlp)
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| Challenge: | Existing studies on the robustness of Large Language Models (LLMs) overlook the interconnected relationships between RAG components or the potential threats prevalent in real-world databases, such as minor textual errors. |
| Approach: | They propose a novel attack method that exploits vulnerabilities in RAG components and tests its robustness against noisy documents. |
| Outcome: | The proposed method devastates the performance of each component and their synergy, and significantly devases the performance. |
Efficient Latent Semantic Clustering for Scaling Test-Time Computation of LLMs (2025.findings-emnlp)
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| Challenge: | Existing methods for scaling test-time computation rely on external models that introduce substantial computational overhead and fail to capture context-aware semantics. |
| Approach: | They propose a method that leverages the generator LLM’s internal hidden states for clustering, eliminating the need for external models. |
| Outcome: | The proposed method improves the computational efficiency of test-time scaling while maintaining or exceeding the performance of existing methods. |
Morpheme Matters: Morpheme-Based Subword Tokenization for Korean Language Models (2026.eacl-short)
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| Challenge: | Existing tokenizers rely on frequency-based segmentation to represent words . this often leads to inefficient token representations and oversegmentation . |
| Approach: | They propose a tokenization method that emphasizes the importance of Korean morphological structures in eojeol. |
| Outcome: | The proposed method outperforms existing tokenizers on Korean benchmark tasks and produces significantly fewer tokens per input sequence. |
Retrieval-Augmented Generation with Estimation of Source Reliability (2025.emnlp-main)
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| Challenge: | Retrieval-Augmented Generation (RAG) is an effective approach to enhance the factual accuracy of large language models (LLMs). |
| Approach: | They propose a multi-source RAG framework that estimates the reliability of sources and prioritizes highly reliable and relevant documents. |
| Outcome: | The proposed framework outperforms baselines in scenarios with heterogeneous source reliability while scaling efficiently as the number of sources increases. |