We lifted 4 functions out of this paper's own repositories and ran 4 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| allenai/unqover | canonical | 4 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| compose_subj_act_bij_line | Ran | allenai/unqover/templates/generate_underspecified_templates.py code served (permissive licence) · get_code("cf56d483faf1a2b7") |
| load_gender_names | Ran | allenai/unqover/lm/predict.py code served (permissive licence) · get_code("293269302d7c4974") |
| load_input | Ran | allenai/unqover/lm/predict.py code served (permissive licence) · get_code("3cc773e67543344d") |
| preprocess | Ran | allenai/unqover/lm/predict.py code served (permissive licence) · get_code("4018efb99d4c77a3") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
While language embeddings have been shown to have stereotyping biases, how these biases affect downstream question answering (QA) models remains unexplored. We present UNQOVER, a general framework to probe and quantify biases through underspecified questions. We show that a naive use of model scores can lead to incorrect bias estimates due to two forms of reasoning errors: positional dependence and question independence. We design a formalism that isolates the aforementioned errors. As case studies, we use this metric to analyze four important classes of stereotypes: gender, nationality, ethnicity, and religion. We probe five transformer-based QA models trained on two QA datasets, along with their underlying language models. Our broad study reveals that (1) all these models, with and without fine-tuning, have notable stereotyping biases in these classes; (2) larger models often have higher bias; and (3) the effect of fine-tuning on bias varies strongly with the dataset and the model size.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2010.02428")
get_code_for_paper("2010.02428")
have("2010.02428")
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