nguyenvuthientrang

nguyenvuthientrang's Stars

• OpenLMLab/LEval

  [ACL'24 Outstanding] Data and code for L-Eval, a comprehensive long context language models evaluation benchmark

  Language:Python37514

• p1nksnow/MoICE

  Official implementation for "Mixture of In-Context Experts Enhance LLMs’ Awareness of Long Contexts" (Accepted by Neurips2024)

  Language:Python112

• lancopku/agent-backdoor-attacks

  Code&Data for the paper "Watch Out for Your Agents! Investigating Backdoor Threats to LLM-Based Agents" [NeurIPS 2024]

  Language:Python663

• tencent-ailab/IP-Adapter

  The image prompt adapter is designed to enable a pretrained text-to-image diffusion model to generate images with image prompt.

  Language:Jupyter Notebook5.8k372

• MadryLab/label-consistent-backdoor-code

  Code for "Label-Consistent Backdoor Attacks"

  Language:Python5410

• AISafety-HKUST/Backdoor_Safety_Tuning

  Backdoor Safety Tuning (NeurIPS 2023 & 2024 Spotlight)

  Language:Jupyter Notebook251

• YiZeng623/I-BAU

  Official Implementation of ICLR 2022 paper, ``Adversarial Unlearning of Backdoors via Implicit Hypergradient''

  Language:Jupyter Notebook5413

• VinAIResearch/Warping-based_Backdoor_Attack-release

  WaNet - Imperceptible Warping-based Backdoor Attack (ICLR 2021)

  Language:Python12118

• clovaai/SSUL

  [NeurIPS 2021] SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning

  Language:Python628

• monster119120/Generalizable-Mixture-of-Experts

  GMoE could be the next backbone model for many kinds of generalization task.

  Language:Python2

• SWY666/SSL-backdoor-BLTO

  This is the code repo for our ICLR 2024 paper "Backdoor Contrastive Learning via Bi-level Trigger Optimization".

  Language:Python8

• sunh1113/Efficient-backdoor-attacks-for-deep-neural-networks-in-real-world-scenarios

  Language:Python5

• TOM-tym/APG

  Official PyTorch implementation of our ICCV2023 paper “When Prompt-based Incremental Learning Does Not Meet Strong Pretraining”

  Language:Python16

• muzairkhattak/PromptSRC

  [ICCV'23 Main Track, WECIA'23 Oral] Official repository of paper titled "Self-regulating Prompts: Foundational Model Adaptation without Forgetting".

  Language:Python26012

• KU-VGI/Si-Blurry

  Official repository for Online Class Incremental Learning on Stochastic Blurry Task Boundary via Mask and Visual Prompt Tuning on ICCV 2023

  Language:Python27

• JingyangQiao/prompt-gradient-projection

  Language:Python262

• thu-ml/HiDe-Prompt

  Hierarchical Decomposition of Prompt-Based Continual Learning: Rethinking Obscured Sub-optimality (NeurIPS 2023, Spotlight)

  Language:Python828

• zihao-ai/Awesome-Backdoor-in-Deep-Learning

  A curated list of papers & resources on backdoor attacks and defenses in deep learning.

  Language:Python19815

• laekov/fastmoe

  A fast MoE impl for PyTorch

  Language:Python1.7k194

• XiangLi1999/ContrastiveDecoding

  contrastive decoding

  Language:Python19814

• Timothyxxx/Chain-of-ThoughtsPapers

  A trend starts from "Chain of Thought Prompting Elicits Reasoning in Large Language Models".

  2k135

• ucinlp/autoprompt

  AutoPrompt: Automatic Prompt Construction for Masked Language Models.

  Language:Python61683

• princeton-nlp/LM-BFF

  [ACL 2021] LM-BFF: Better Few-shot Fine-tuning of Language Models https://arxiv.org/abs/2012.15723

  Language:Python728132

• lucidrains/CALM-pytorch

  Implementation of CALM from the paper "LLM Augmented LLMs: Expanding Capabilities through Composition", out of Google Deepmind

  Language:Python17411

• keirp/automatic_prompt_engineer

  Language:Python1.2k160

• allenai/natural-instructions

  Expanding natural instructions

  Language:Python987192

• google-research/FLAN

  Language:Python1.5k160

• amazon-science/auto-cot

  Official implementation for "Automatic Chain of Thought Prompting in Large Language Models" (stay tuned & more will be updated)

  Language:Jupyter Notebook1.8k163

• dqxiu/ICL_PaperList

  Paper List for In-context Learning 🌷

  85160

• r-three/t-few

  Code for T-Few from "Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning"

  Language:Python44862