Papers by Jeongyeon Hwang
Lossless Acceleration of Large Language Models with Hierarchical Drafting based on Temporal Locality in Speculative Decoding (2025.findings-naacl)
Copied to clipboard
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)
Copied to clipboard
| 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)
Copied to clipboard
| 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. |
Retrieval-Augmented Generation with Estimation of Source Reliability (2025.emnlp-main)
Copied to clipboard
| 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. |