Skip to content

README.md

Latest commit

 

History

History
19 lines (12 loc) · 1.2 KB

README.md

File metadata and controls

19 lines (12 loc) · 1.2 KB

Causal Representation Learning Approach for Unlearning Toxic Content in LLMs

Dataset

Dataset 1: RealToxicityPrompts [Dataset Link] | [Paper]

Run ROME with GPT2

Notes:

  • The entire codebase for ROME can be found in the directory rome-main. Cloned from Locating and Editing Factual Associations in GPT (NeurIPS'22).
  • The rome-main/trace_main.py is the main script to run a vanilla example of causal tracing on one of the datasets in the original paper.
  • The directory rome-main/dsets contains the datasets they use. This is where we need to add the RealToxicityPrompts dataset and load it from for inference. It is present in the file rome-main/dsets/realtoxicityprompts.py

For William:

  • Refer to the RealToxicityPrompts paper to determine which model they used and pick one of the GPT-2 variants to run ROME with the prompts from the dataset.
  • Choose the "challenging" subset of prompts, i.e., where dataset["challenging"] == true
  • For our use case, right now just the causal tracing part is enough, we don't need to worry about the editing part yet.