Evaluating the Ripple Effects of Knowledge Editing in Language Models

This repository contains the official code of the paper: "Evaluating the Ripple Effects of Knowledge Editing in Language Models".

Setup

The benchmark creation and all experiments and evaluations were conducted in a Python 3.9 environment. To clone the repository and set up the environment, please run the following commands:

git clone https://github.com/edenbiran/RippleEdits.git
cd RippleEdits
pip install -r requirements.txt

RippleEdits Benchmark

The benchmark files and statistics can be found under data/benchmark/ and data/stats/. The benchmark is split into three files named according to the benchmark`s three subsets: RECENT, RANDOM and POPULAR. For more details please refer to section 4 of the paper.

The source code for generating the benchmark can be found under src/. Generating the benchmark from scratch can be done using src/build_benchmark.py. Benchmark popularity statistics can be extracted using src/benchmark_statistics.py.

Each benchmark json contains a list of entries. Each entry is an edit containing the edit information (which also contains the original fact if applicable) and the 6 evaluation criteria. Each evaluation criteria contains a list of tests, where each test contains the test prompt, answers and conditions. An example (shortened for brevity) of an edit entry can be seen below:

{
  "example_type": "popular",
  "edit": {
    "prompt": "The name of the country of citizenship of Leonardo DiCaprio is Syria.",
    "subject_id": "Q38111",
    "relation": "COUNTRY_OF_CITIZENSHIP",
    "target_id": "Q858",
    "original_fact": {
      "prompt": "The name of the country of citizenship of Leonardo DiCaprio is United States of America.",
      "subject_id": "Q38111",
      "relation": "COUNTRY_OF_CITIZENSHIP",
      "target_id": "Q30"
    }
  },
  "Relation_Specifity": [
    {
      "test_queries": [
        {
          "prompt": "The name of the mother of Leonardo DiCaprio is",
          "answers": [
            {
              "value": "Irmelin DiCaprio",
              "aliases": [
                "Irmelin Indenbirken",
                "Irmelin Indenbirken-DiCaprio"
              ]
            }
          ],
          "query_type": "regular",
          "subject_id": "Q38111",
          "relation": "MOTHER",
          "target_ids": [
            "Q22984557"
          ],
          "phrase": null
        }
      ],
      "test_condition": "OR",
      "condition_queries": [
        {
          "prompt": "The name of the mother of Leonardo DiCaprio is",
          "answers": [
            {
              "value": "Irmelin DiCaprio",
              "aliases": [
                "Irmelin Indenbirken",
                "Irmelin Indenbirken-DiCaprio"
              ]
            }
          ],
          "query_type": "regular",
          "subject_id": "Q38111",
          "relation": "MOTHER",
          "target_ids": [
            "Q22984557"
          ],
          "phrase": null
        }
      ]
    },
  ...
  ],
  "Logical_Generalization": [...],
  "Subject_Aliasing": [...],
  "Compositionality_I": [...],
  "Compositionality_II": [...],
  "Forgetfulness": [...]
}

Evaluation

The source code for all evaluations of the benchmark can be found under src/. All evaluations can be conducted using src/evaluation.py.

In order to evaluate the benchmark on a language model not currently supported extend the class QueryExecutor in src/queryexecutor.py and add the new QueryExecutor to src/evaluation.py.

In order to evaluate the benchmark on a knowledge editing technique not currently supported extend the class ModelEditor in src/modeleditor.py and add the new ModelEditor to src/evaluation.py.

Citation

@article{cohen2024evaluating,
  title={Evaluating the ripple effects of knowledge editing in language models},
  author={Cohen, Roi and Biran, Eden and Yoran, Ori and Globerson, Amir and Geva, Mor},
  journal={Transactions of the Association for Computational Linguistics},
  volume={12},
  pages={283--298},
  year={2024},
  publisher={MIT Press One Broadway, 12th Floor, Cambridge, Massachusetts 02142, USA~…}
}