Antonios Anastasopoulos, Ziwei Zhu, Chahat Raj, Anjishnu Mukherjee
We lifted 7 functions out of this paper's own repositories and ran 1 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 |
|---|---|---|
| iamshnoo/weathub | canonical | 1 of 7 |
| Function | Status | Where it lives |
|---|---|---|
| embedding_variance | Ran | iamshnoo/weathub/src/compare_embeddings.py pointer only (licence: GPL-3.0) · get_code("c8721af1c24e064f") |
| get_weat_google_human | Not yet run | iamshnoo/weathub/src/load_annotations.py pointer only (licence: GPL-3.0) · get_code("78828d54d4541067") |
| load_hf_tokenizer_model | Not yet run | iamshnoo/weathub/src/encoding_utils.py pointer only (licence: GPL-3.0) · get_code("d77adb941faf57ff") |
| process_annotation_file | Not yet run | iamshnoo/weathub/src/load_annotations.py pointer only (licence: GPL-3.0) · get_code("500e9ab633684d53") |
| process_sheet_one | Not yet run | iamshnoo/weathub/src/load_annotations.py pointer only (licence: GPL-3.0) · get_code("67f8b72607a187d2") |
| save_results_to_df | Not yet run | iamshnoo/weathub/src/run_weat.py pointer only (licence: GPL-3.0) · get_code("1eab4d0c275953ef") |
| save_results_to_df | Not yet run | iamshnoo/weathub/src/valence_weat.py pointer only (licence: GPL-3.0) · get_code("b8ea64c8aee135eb") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Human biases are ubiquitous but not uniform: disparities exist across linguistic, cultural, and societal borders. As large amounts of recent literature suggest, language models (LMs) trained on human data can reflect and often amplify the effects of these social biases. However, the vast majority of existing studies on bias are heavily skewed towards Western and European languages. In this work, we scale the Word Embedding Association Test (WEAT) to 24 languages, enabling broader studies and yielding interesting findings about LM bias. We additionally enhance this data with culturally relevant information for each language, capturing local contexts on a global scale. Further, to encompass more widely prevalent societal biases, we examine new bias dimensions across toxicity, ableism, and more. Moreover, we delve deeper into the Indian linguistic landscape, conducting a comprehensive regional bias analysis across six prevalent Indian languages. Finally, we highlight the significance of these social biases and the new dimensions through an extensive comparison of embedding methods, reinforcing the need to address them in pursuit of more equitable language models. 1 * Equal contribution 1 All code, data and results are available here: https:// github.com/iamshnoo/weathub.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2310.17586")
get_code_for_paper("2310.17586")
have("2310.17586")
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