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Large Language Models (LLMs) inherently carry the biases contained in their training corpora, which can lead to the perpetuation of societal harm.
The proof and measurement of association between two things
Spearman, C · 1961
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Data preprocessing techniques for classification without discrimination
Kamiran, F. and Calders, T · 2012
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Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Chouldechova, A · 2016
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Equality of opportunity in supervised learning
Hardt, M., Price, E., and Srebro, N · 2016
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Combining satellite imagery and machine learning to predict poverty
Jean, N., Burke, M., Xie, S. M., Davis, W. M., Lobell, D., and Ermon, S · 2016
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Viirs night-time lights
Elvidge, C. D., Baugh, K. E., Zhizhin, M. N., Hsu, F.-C., and Ghosh, T · 2017
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Word embeddings quantify 100 years of gender and ethnic stereotypes
Garg, N., Schiebinger, L., Jurafsky, D., and Zou, J. Y · 2017
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Poverty prediction with public landsat 7 satellite imagery and machine learning
Perez, A., Yeh, C., Azzari, G., Burke, M., Lobell, D., and Ermon, S · 2017
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Worldpop, open data for spatial demography
Tatem, A. J · 2017
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Addressing age-related bias in sentiment analysis
Diaz, M., Johnson, I. L., Lazar, A., Piper, A. M., and Gergle, D · 2018
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Data from: Climatologies at high resolution for the earth’s land surface areas
Karger, D. N., Conrad, O., Böhner, J., Kawohl, T., Kreft, H., Soria-Auza, R. W., Zimmermann, N. E., Linder, H. P., and Kessler, M · 2018
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Reducing gender bias in abusive language detection
Park, J. H., Shin, J., and Fung, P · 2018
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Global high resolution population denominators project - funded by the bill and melinda gates foundation, 2018
WorldPop and CIESIN, C. U · 2018
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Toward gender-inclusive coreference resolution
Cao, Y. T. and Daumé, H · 2019
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GHSL Data Package 2019
Florczyk, A. J., Corbane, C., Ehrlich, D., Freire, S., Kemper, T., Maffenini, L., Melchiorri, M., Pesaresi, M., Politis, P., Schiavina, M., Sabo, F., and Zanchetta, L · 2019
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End-to-end bias mitigation by modelling biases in corpora
Mahabadi, R. K., Belinkov, Y., and Henderson, J · 2019
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Black is to criminal as caucasian is to police: Detecting and removing multiclass bias in word embeddings
Manzini, T., Lim, Y. C., Tsvetkov, Y., and Black, A. W · 2019
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Assessing social and intersectional biases in contextualized word representations
Tan, Y. C. and Celis, E · 2019
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Unmasking contextual stereotypes: Measuring and mitigating bert’s gender bias
Bartl, M., Nissim, M., and Gatt, A · 2020
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A survey of bias in machine learning through the prism of statistical parity
Besse, P. C., del Barrio, E., Gordaliza, P., Loubes, J.-M., and Risser, L · 2020
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Realtoxicityprompts: Evaluating neural toxic degeneration in language models
Gehman, S., Gururangan, S., Sap, M., Choi, Y., and Smith, N. A · 2020
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Unqovering stereotypical biases via underspecified questions
Li, T., Khashabi, D., Khot, T., Sabharwal, A., and Srikumar, V · 2020
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Stereoset: Measuring stereotypical bias in pretrained language models
Nadeem, M., Bethke, A., and Reddy, S · 2020
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Crows-pairs: A challenge dataset for measuring social biases in masked language models
Nangia, N., Vania, C., Bhalerao, R., and Bowman, S. R · 2020
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Measuring and reducing gendered correlations in pre-trained models
Webster, K., Wang, X., Tenney, I., Beutel, A., Pitler, E., Pavlick, E., Chen, J., and Petrov, S · 2020
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“i’m sorry to hear that”: Finding new biases in language models with a holistic descriptor dataset
Smith, E. M., Hall, M., Kambadur, M., Presani, E., and Williams, A · 2022
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Evaluation of gpt-3.5 and gpt-4 for supporting real-world information needs in healthcare delivery
Dash, D., Thapa, R., Banda, J., Swaminathan, A., Cheatham, M., Kashyap, M., Kotecha, N., Chen, J. H., Gombar, S., Downing, L., Pedreira, R. A., Goh, E., Arnaout, A., Morris, G. K., Magon, H., Lungren, M. P., Horvitz, E., and Shah, N. H · 2023
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Language modeling is compression
Del’etang, G., Ruoss, A., Duquenne, P.-A., Catt, E., Genewein, T., Mattern, C., Grau-Moya, J., Li, W. K., Aitchison, M., Orseau, L., Hutter, M., and Veness, J · 2023
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Learning a foundation language model for geoscience knowledge understanding and utilization
Deng, C., Zhang, T., He, Z., Chen, Q., Shi, Y., Zhou, L., Fu, L., Zhang, W., Wang, X., Zhou, C., et al · 2023
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Using publicly available satellite imagery and deep learning to understand economic well-being in africa
Yeh, C., Perez, A., Driscoll, A., Azzari, G., Tang, Z., Lobell, D., Ermon, S., and Burke, M · 2020
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Persistent anti-muslim bias in large language models
Abid, A., Farooqi, M., and Zou, J. Y · 2021
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Mitigating language-dependent ethnic bias in bert
Ahn, J. and Oh, A. H · 2021
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On the opportunities and risks of foundation models
Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al · 2021
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Stereotype and skew: Quantifying gender bias in pre-trained and fine-tuned language models
de Vassimon Manela, D., Errington, D., Fisher, T., van Breugel, B., and Minervini, P · 2021
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Bold: Dataset and metrics for measuring biases in open-ended language generation
Dhamala, J., Sun, T., Kumar, V., Krishna, S., Pruksachatkun, Y., Chang, K.-W., and Gupta, R · 2021
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Documenting large webtext corpora: A case study on the colossal clean crawled corpus
Dodge, J., Marasovic, A., Ilharco, G., Groeneveld, D., Mitchell, M., and Gardner, M · 2021
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Gallegos, I. O., Rossi, R. A., Barrow, J., Tanjim, M. M., Kim, S., Dernoncourt, F., Yu, T., Zhang, R., and Ahmed, N · 2023
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Gemini: A family of highly capable multimodal models
Google · 2023
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Survey on sociodemographic bias in natural language processing
Gupta, V., Venkit, P. N., Wilson, S., and Passonneau, R · 2023
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Trustgpt: A benchmark for trustworthy and responsible large language models
Huang, Y., Zhang, Q., Yu, P. S., and Sun, L · 2023
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On the opportunities and challenges of foundation models for geospatial artificial intelligence
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Geollm: Extracting geospatial knowledge from large language models
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Llama 2: Open foundation and fine-tuned chat models
Meta · 2023
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Unmasking nationality bias: A study of human perception of nationalities in ai-generated articles
Venkit, P. N., Gautam, S., Panchanadikar, R., Huang, T., and Wilson, S · 2023
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Tackling bias in pre-trained language models: Current trends and under-represented societies
Yogarajan, V., Dobbie, G., Keegan, T. T., and Neuwirth, R. J · 2023
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Geogpt: Understanding and processing geospatial tasks through an autonomous gpt
Zhang, Y., Wei, C., Wu, S., He, Z., and Yu, W · 2023
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A survey of large language models
Zhao, W. X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., Min, Y., Zhang, B., Zhang, J., Dong, Z., Du, Y., Yang, C., Chen, Y., Chen, Z., Jiang, J., Ren, R., Li, Y., Tang, X., Liu, Z., Liu, P., Nie, J., and rong Wen, J · 2023
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Global subnational infant mortality rates, version 2.01, 2021
CIESIN · 2024
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Global human settlement layer: Population and built-up estimates, and degree of urbanization settlement model grid, 2021
JRC and CIESIN · 2024
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Global-liar: Factuality of llms over time and geographic regions, 2024
Mirza, S., Coelho, B., Cui, Y., Pöpper, C., and McCoy, D · 2024
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Mistral · 2024
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Multi-fact: Assessing multilingual llms’ multi-regional knowledge using factscore, 2024
Shafayat, S., Kim, E., Oh, J., and Oh, A · 2024
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