NLP113 [2025-1] 김학선 - Code Security Vulnerability Repair Using Reinforcement Learning with Large Language Models https://arxiv.org/abs/2401.07031 Code Security Vulnerability Repair Using Reinforcement Learning with Large Language ModelsWith the recent advancement of Large Language Models (LLMs), generating functionally correct code has become less complicated for a wide array of developers. While using LLMs has sped up the functional development process, it poses a heavy risk to code secarxiv.orgIntroducti.. 2025. 2. 18. [2025-1] 차승우 - Titans: Learning to Memorize at Test Time https://arxiv.org/abs/2501.00663 Titans: Learning to Memorize at Test TimeOver more than a decade there has been an extensive research effort on how to effectively utilize recurrent models and attention. While recurrent models aim to compress the data into a fixed-size memory (called hidden state), attention allows attending toarxiv.org 0. Abstract 순환 모델은 데이터를 고정된 크기의 메모리(hidden state)로 압축하는 것을 .. 2025. 2. 17. [2025-1] 차승우 - Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling https://arxiv.org/abs/1412.3555 Empirical Evaluation of Gated Recurrent Neural Networks on Sequence ModelingIn this paper we compare different types of recurrent units in recurrent neural networks (RNNs). Especially, we focus on more sophisticated units that implement a gating mechanism, such as a long short-term memory (LSTM) unit and a recently proposed gatedarxiv.org0. Abstract- tanh RNN과 비교하.. 2025. 2. 15. [2025-1] 임재열 - Hymba: A Hybrid-head Architecture for Small Language Models Hymba는 2024년 NVIDIA에서 제안한 모델입니다. [Hymba]https://arxiv.org/abs/2411.13676 Hymba: A Hybrid-head Architecture for Small Language ModelsWe propose Hymba, a family of small language models featuring a hybrid-head parallel architecture that integrates transformer attention mechanisms with state space models (SSMs) for enhanced efficiency. Attention heads provide high-resolution recall, whilearxiv.org*.. 2025. 2. 12. 이전 1 ··· 13 14 15 16 17 18 19 ··· 29 다음
