snu-mllab / Neural-Relation-GraphLinks
Official PyTorch implementation of "Neural Relation Graph: A Unified Framework for Identifying Label Noise and Outlier Data" (NeurIPS'23)
☆15Updated last year
Alternatives and similar repositories for Neural-Relation-Graph
Users that are interested in Neural-Relation-Graph are comparing it to the libraries listed below
Sorting:
- Data for "Datamodels: Predicting Predictions with Training Data"☆97Updated 2 years ago
- ☆29Updated 2 years ago
- ☆27Updated 5 months ago
- ☆20Updated last year
- ☆29Updated 2 years ago
- Is In-Context Learning Sufficient for Instruction Following in LLMs? [ICLR 2025]☆30Updated 5 months ago
- Code for T-MARS data filtering☆35Updated last year
- ☆26Updated last year
- Recycling diverse models☆45Updated 2 years ago
- [NeurIPS 2024] Goldfish Loss: Mitigating Memorization in Generative LLMs☆90Updated 8 months ago
- This is the oficial repository for "Safer-Instruct: Aligning Language Models with Automated Preference Data"☆17Updated last year
- Latest Weight Averaging (NeurIPS HITY 2022)☆30Updated 2 years ago
- The repository contains code for Adaptive Data Optimization☆25Updated 7 months ago
- Official repository of "LiNeS: Post-training Layer Scaling Prevents Forgetting and Enhances Model Merging"☆29Updated 8 months ago
- ☆33Updated 6 months ago
- We introduce EMMET and unify model editing with popular algorithms ROME and MEMIT.☆24Updated 7 months ago
- Aioli: A unified optimization framework for language model data mixing☆27Updated 5 months ago
- One Initialization to Rule them All: Fine-tuning via Explained Variance Adaptation☆40Updated 9 months ago
- Official implementation of FIND (NeurIPS '23) Function Interpretation Benchmark and Automated Interpretability Agents☆49Updated 9 months ago
- ☆17Updated last year
- Stanford NLP Python library for benchmarking the utility of LLM interpretability methods☆99Updated 3 weeks ago
- Implementation for the paper "Fictitious Synthetic Data Can Improve LLM Factuality via Prerequisite Learning"☆10Updated 6 months ago
- ☆27Updated 2 years ago
- PaCE: Parsimonious Concept Engineering for Large Language Models (NeurIPS 2024)☆38Updated 8 months ago
- ☆45Updated last year
- Revisiting Efficient Training Algorithms For Transformer-based Language Models (NeurIPS 2023)☆80Updated last year
- ☆28Updated 4 months ago
- An official implementation of "Catastrophic Failure of LLM Unlearning via Quantization" (ICLR 2025)☆27Updated 4 months ago
- ☆22Updated 9 months ago
- ☆18Updated 2 years ago