Name: Seungone Kim
Type: User
Company: Carnegie Mellon University
Bio: Incoming Ph.D. student at CMU LTI researching LLM Evaluation. Prev. KAIST AI, Yonsei Univ, NAVER AI Lab, LG AI Research #nlproc
Twitter: seungonekim
Location: Pittsburgh, US
Blog: seungonekim.github.io
Seungone Kim's Projects
Reproduce results and replicate training fo T0 (Multitask Prompted Training Enables Zero-Shot Task Generalization)
Adding new tasks to T0 without catastrophic forgetting
Tk-Instruct is a Transformer model that is tuned to solve many NLP tasks by following instructions.
'Vaswani, Ashish, et al. "Attention is all you need." Advances in neural information processing systems. 2017.' (implementation from scratch with Pytorch)
🤗 Transformers: State-of-the-art Natural Language Processing for Pytorch, TensorFlow, and JAX.
Train transformer language models with reinforcement learning.
A repo for distributed training of language models with Reinforcement Learning via Human Feedback (RLHF)
The first version of the Nuhnadulee Model (Vanilla Base Code)
Bringing large-language models and chat to web browsers. Everything runs inside the browser with no server support.
This repository includes projects done in YBIGTA 2021 spring session(연세대학교 빅데이터 학회).
This repository contains presentation materials, links to presentation videos, and a summary of all the papers we have studied in Yonsei NLP Study Season1(2021.07.07~2021.08.25). Our main topic was Pretraining methods of Language Models, and other approaches to build up a better NLP Model. We have covered 37 papers in total!
This repository contains presentation materials, links to presentation videos, and a summary of all the papers we have studied in Yonsei NLP Study Season2(2021.09.08~2021.11.17). Our Main Topic was Question Answering and the various techniques needed to solve QA. We have covered 55 papers in total!
This repository contains presentation materials, links to presentation videos, and a summary of all the papers we have studied in Yonsei NLP Study Season3(2022.01.01~2022.03.01). Our Main Topic was Question Answering and Knowledge Distillation. We have covered 23 papers in total!
Prod Env