Transformer memory mass-editor (ICLR 2023 research paper)
Top 62.1% on sourcepulse
MEMIT enables efficient, large-scale factual editing within pre-trained transformer language models. It targets researchers and practitioners seeking to correct or update knowledge embedded in LLMs without full retraining. The primary benefit is the ability to modify thousands of facts with minimal computational overhead compared to fine-tuning.
How It Works
MEMIT operates by identifying and modifying specific weights within the transformer's attention layers. It formulates factual edits as targeted interventions, calculating necessary weight adjustments to steer the model's output towards the desired new fact. This approach avoids catastrophic forgetting and allows for precise, localized modifications.
Quick Start & Requirements
bash ./scripts/setup_conda.sh $CONDA_HOME
.notebooks/memit.ipynb
.experiments/evaluate.py
.Highlighted Details
Maintenance & Community
The project is associated with ICLR 2023 and authored by Kevin Meng et al. No specific community channels or active maintenance signals are provided in the README.
Licensing & Compatibility
The project is released under an unspecified license. The README does not detail licensing terms or compatibility for commercial use.
Limitations & Caveats
The README does not specify the license, making commercial use uncertain. It also lacks explicit details on supported model architectures beyond the GPT-J-6B example.
1 year ago
1+ week