LLM(23)
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[논문리뷰] AI models collapse when trained on recursively generated data
Shumailov, I., Shumaylov, Z., Zhao, Y. et al. AI models collapse when trained on recursively generated data. Nature 631, 755–759 (2024). https://doi.org/10.1038/s41586-024-07566-y 2024년 7월 Nature 저널에 등록된 article이다. 요즘 LLM에서 대부분의 논문들이 model의 성능을 높이거나 효율성을 향상시키는 방향이라는 점과 대비되는 논문인데, 이 논문은 생성형 AI가 만든 데이터를 LLM에 다시 학습을 시켰을 때 일어날 수 있는 model collapse 현상에 대해서 다루는 논문이라 신선했다. AbstractStable diffusion은 imag..
2024.09.27 -
[논문리뷰] Direct Preference Optimization: Your Language Model is Secretly a Reward Model (DPO)
Direct Preference Optimization: Your Language Model is Secretly a Reward Model, Advances in Neural Information Processing Systems (Neurips,'24). Rafailov, R., Sharma, A., Mitchell, E., Manning, C. D., Ermon, S., & Finn, C.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 ..
2024.09.26 -
[챗GPT 러닝데이| 한국어 LLM 민주화의 시작 KoAlpaca!] 세미나 리뷰
AIFactory (인공지능 팩토리)에서 진행했던 세미나입니다.영상의 길이는 총 1시간 45분 정도입니다.url: https://aifactory.space/task/2415/overview [챗GPT 러닝데이 | 챗GPT말고 LLM] 한국어 LLM 민주화의 시작 KoAlpaca! - 이준범#무료 #유튜브라이브 #5월한달aifactory.space발표자: 이준범님 (이력: 현) Ed-tech 스타트업 데이터드리, 한국어 NLP 오픈소스 프로젝트 개발자, 전) NAVER Clova, NEXON Korea) Intro1. LLM?더보기* 자주 나오는 용어- LM (Language Model): 다음 단어를 맞추는 확률 모델- LLM (Large Language Model): 말 그대로 "큰" 언어 모델- I..
2024.09.24 -
[논문리뷰] LIMA: Less Is More for Alignment
LIMA: Less Is More for Alignment, In Proceedings of the 37th International Conference on Neural Information Processing Systems (NIPS '23). Chunting Zhou, Pengfei Liu, Puxin Xu, Srini Iyer, Jiao Sun, Yuning Mao, Xuezhe Ma, Avia Efrat, Ping Yu, Lili Yu, Susan Zhang, Gargi Ghosh, Mike Lewis, Luke Zettlemoyer, and Omer Levy.https://arxiv.org/abs/2305.11206 LIMA: Less Is More for AlignmentLarge lang..
2024.09.23 -
[논문리뷰] PARDEN, Can You Repeat That? Defending against Jailbreaks via Repetition
PARDEN, Can You Repeat That? Defending against Jailbreaks via Repetition, Accepted at ICML 2024, Ziyang Zhang, Qizhen Zhang, Jakob Foerster https://arxiv.org/abs/2405.07932 PARDEN, Can You Repeat That? Defending against Jailbreaks via RepetitionLarge language models (LLMs) have shown success in many natural language processing tasks. Despite rigorous safety alignment processes, supposedly safety..
2024.09.14