Fighting Bias in Large Language Models

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Large language models form the basis of almost all currently topical AI research, making it vital to identify and rectify different types of demographic biases in those models (be that bias based on gender or sexual identity, or on cultural, ethical or social background, etc.). This has triggered intense research on fair representation in language models, aiming both at building and using unbiased training and evaluation datasets, and at changing the actual learning algorithms themselves. There is still a lot of room for improvements though, both in identifying and quantifying bias, in developing dibiasing methods, and in defining bias as such.