NLP113 [2025-2] 박승원 - Learning representations by back-propagating errors 논문 링크: https://www.cs.utoronto.ca/~hinton/absps/naturebp.pdf 논문의 의의: 본 논문은 Back Propagation(오차역전파)를 인공신경망 학습에 체계적으로 적용하여, 다층 신경망 연구의 토대를 다진 연구.Existing WorksNeural Network를 만드려는 시도가 있었음.Input units과 output units이 직접적으로 연결되는 구조는 학습이 쉬웠으나, 흥미로운 결과가 도출되지는 않았음.Inputs과 outputs 사이에 hidden units이 존재하면 학습이 어려워지지만 더 흥미로운 결과를 도출함.이 hidden units이 어떤 상황에, 얼마 만큼 활성화 될 지를 결정하는 것이 학습에 중요함. Proposed methods같은 l.. 2025. 12. 19. [2025-2] 전연주 - Train-Attention: Meta-Learning Where to Focus in Continual Knowledge Learning 논문 링크: https://arxiv.org/abs/2407.16920 Train-Attention: Meta-Learning Where to Focus in Continual Knowledge LearningPrevious studies on continual knowledge learning (CKL) in large language models (LLMs) have predominantly focused on approaches such as regularization, architectural modifications, and rehearsal techniques to mitigate catastrophic forgetting. However, thesarxiv.orgConference: Neur.. 2025. 12. 6. [2025-2] 박제우 - The Impact of Reasoning Step Length on Large Language Models https://arxiv.org/abs/2401.04925 The Impact of Reasoning Step Length on Large Language ModelsChain of Thought (CoT) is significant in improving the reasoning abilities of large language models (LLMs). However, the correlation between the effectiveness of CoT and the length of reasoning steps in prompts remains largely unknown. To shed light on thiarxiv.org본 논문은 2024 ACL Findings에 등재된 논문으로, 2025년.. 2025. 12. 6. [2025-2] 최민서 - Direct Preference Optimization:Your Language Model is Secretly a Reward Model [논문링크] https://arxiv.org/abs/2305.18290 Direct Preference Optimization: Your Language Model is Secretly a Reward ModelWhile large-scale unsupervised language models (LMs) learn broad world knowledge and some reasoning skills, achieving precise control of their behavior is difficult due to the completely unsupervised nature of their training. Existing methods for gaining sarxiv.org 1. Introductio.. 2025. 11. 19. 이전 1 ··· 4 5 6 7 8 9 10 ··· 29 다음
