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PRML Lab in Korea University
- Seoul
- in/kunwoo-kim-79042a240
Stars
Official repository for "Orthogonal Projection Loss" (ICCV'21)
A resource repository for machine unlearning in large language models
A framework for few-shot evaluation of language models.
[EMNLP 2024 Findings] To Forget or Not? Towards Practical Knowledge Unlearning for Large Language Models
The official code of the paper "A Closer Look at Machine Unlearning for Large Language Models".
[ACL 2024] AUTOACT: Automatic Agent Learning from Scratch for QA via Self-Planning
[EMNLP 2024 Findings] The official PyTorch implementation of EchoSight: Advancing Visual-Language Models with Wiki Knowledge.
Open-source evaluation toolkit of large vision-language models (LVLMs), support 160+ VLMs, 50+ benchmarks
Official PyTorch Implementation of MLLM Is a Strong Reranker: Advancing Multimodal Retrieval-augmented Generation via Knowledge-enhanced Reranking and Noise-injected Training.
High-Quality Human Motion Video Generation with Confidence-aware Pose Guidance
A library for easily merging multiple LLM experts, and efficiently train the merged LLM.
Official Code for EMNLP2023 Main Conference paper: "KCTS: Knowledge-Constrained Tree Search Decoding with Token-Level Hallucination Detection"
Awesome Machine Unlearning (A Survey of Machine Unlearning)
[ACL 2024] An Easy-to-use Knowledge Editing Framework for LLMs.
🧑🏫 60+ Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), ga…
[NeurIPS 2023] Reflexion: Language Agents with Verbal Reinforcement Learning
Can large language models provide useful feedback on research papers? A large-scale empirical analysis.
Aligning pretrained language models with instruction data generated by themselves.
Databricks’ Dolly, a large language model trained on the Databricks Machine Learning Platform
GPT4All: Run Local LLMs on Any Device. Open-source and available for commercial use.
CVNets: A library for training computer vision networks