metadata

license: mit
configs:
  - config_name: static
    data_files:
      - split: test
        path: static.csv
  - config_name: temporal
    data_files:
      - split: test
        path: temporal.csv
  - config_name: disputable
    data_files:
      - split: test
        path: disputable.csv
task_categories:
  - question-answering
language:
  - en
pretty_name: DynamicQA
size_categories:
  - 10K<n<100K

DYNAMICQA

This is a repository for the paper DYNAMICQA: Tracing Internal Knowledge Conflicts in Language Models accepted at Findings of EMNLP 2024.

main_figure

Our paper investigates the Language Model's behaviour when the conflicting knowledge exist within the LM's parameters. We present a novel dataset containing inherently conflicting data, DYNAMICQA. Our dataset consists of three partitions, Static, Disputable 🤷‍♀️, and Temporal 🕰️.

We also evaluate several measures on their ability to reflect the presence of intra-memory conflict: Semantic Entropy and a novel Coherent Persuasion Score. You can find our findings from our paper!

The implementation of the measures is available on our github repo!

Dataset

Our dataset consists of three different partitions.

Partition	Number of Questions
Static	2500
Temporal	2495
Disputable	694

Details

Question : "question" column
Answers : Two different answers are available: one in the "obj" column and the other in the "replace_name" column.
Context : Context ("context" column) is masked with [ENTITY]. Before providing the context to the LM, you should replace [ENTITY] with either "obj" or "replace_name".
Number of edits : "num_edits" column. This denotes Temporality for temporal partition, and Disputability for disputable partition.

Citation

If you find our dataset helpful, kindly refer to us in your work using the following citation:

@inproceedings{marjanović2024dynamicqatracinginternalknowledge,
      title={DYNAMICQA: Tracing Internal Knowledge Conflicts in Language Models}, 
      author={Sara Vera Marjanović and Haeun Yu and Pepa Atanasova and Maria Maistro and Christina Lioma and Isabelle Augenstein},
      year={2024},
      booktitle = {Findings of EMNLP},
      publisher = {Association for Computational Linguistics}
}