An Empirical Study on the Characteristics of Bias upon Context Length Variation for Bangla (2024.findings-acl)
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| Challenge: | Language models exhibit various social biases due to widespread usage. |
| Approach: | They extend existing methods for measuring gender bias in Bangla by examining context length variation. |
| Outcome: | The proposed method relies on context length variation, highlighting the need for nuanced considerations in Bangla bias analysis. |
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| Challenge: | Existing work has demonstrated the ability of large language models to learn lexical and label biases in-context negatively impacts performance and robustness of models. |
| Approach: | They investigate the impact of length biases on in-context learning by analyzing model length information in-constext. |
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| Challenge: | Standard bias benchmarks are used for large language models to measure the association between social attributes and single-word outputs. |
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| Challenge: | Pretrained multilingual models exhibit the same social bias as models processing English texts. |
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| Challenge: | Neural Machine Translation systems are prone to gender biases in their learned representations. |
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| Challenge: | Existing discriminatory biases in training data can be amplified by models . text corpora exhibit socially problematic biase . |
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BanStereoSet: A Dataset to Measure Stereotypical Social Biases in LLMs for Bangla (2025.findings-acl)
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| Challenge: | ***BanStereoSet*** is a dataset designed to evaluate stereotypical social biases in multilingual LLMs for the Bangla language. |
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| Challenge: | Recent advances in self-supervised training have led to a new class of pretrained vision–language models. |
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