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20242026
most citedSuRe: Summarizing Retrievals using Answer Candidates for Open-domain QA of LLMs

4 citations · 10 across the 17 of their papers we have counts for

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cs.CL2026

RedacBench: Can AI Erase Your Secrets?

Hyunjun Jeon, Kyuyoung Kim, Jinwoo Shin

Modern language models can readily extract sensitive information from unstructured text, making redaction -- the selective removal of such information -- critical for data security…

cs.CL2025

Accelerated Test-Time Scaling with Model-Free Speculative Sampling

Woomin Song, Saket Dingliwal, Sai Muralidhar Jayanthi +4

Language models have demonstrated remarkable capabilities in reasoning tasks through test-time scaling techniques like best-of-N sampling and tree search. However, these approaches…

cs.CL2025

Compress, Gather, and Recompute: REFORMing Long-Context Processing in Transformers

Woomin Song, Sai Muralidhar Jayanthi, Srikanth Ronanki +5

As large language models increasingly gain popularity in real-world applications, processing extremely long contexts, often exceeding the model's pre-trained context limits, has em…

cs.CL2025

Mamba Drafters for Speculative Decoding

Daewon Choi, Seunghyuk Oh, Saket Dingliwal +9

Speculative decoding has emerged as a promising approach to accelerating large language model (LLM) generation using a fast drafter while maintaining alignment with the target mode…

cs.CL2025

Personalized Language Models via Privacy-Preserving Evolutionary Model Merging

Kyuyoung Kim, Jinwoo Shin, Jaehyung Kim

Personalization in language models aims to tailor model behavior to individual users or user groups. Prompt-based methods incorporate user preferences into queries, while training-…

cs.CL2024

Tabular Transfer Learning via Prompting LLMs

Jaehyun Nam, Woomin Song, Seong Hyeon Park +5

Learning with a limited number of labeled data is a central problem in real-world applications of machine learning, as it is often expensive to obtain annotations. To deal with the…

cs.CL2024★ 4 cited

SuRe: Summarizing Retrievals using Answer Candidates for Open-domain QA of LLMs

Jaehyung Kim, Jaehyun Nam, Sangwoo Mo +5

Large language models (LLMs) have made significant advancements in various natural language processing tasks, including question answering (QA) tasks. While incorporating new infor…