Understanding the Origins of Bias in Word Embeddings — mentioned by mdifrgechd

Sun, 25 Oct 2020 15:56:57 GMT

One possible counterpoint- sample based explanation techniques can tell what training data was most influential in a ML model's prediction, and this has been considered in language models[0]. So you could argue that if there are training examples within a corpus that are having an outsized influence on the model output, then maybe it is derivative. This would be pretty cool to look at - are some GPT or other language model outputs relying strongly on a few sources? [0] https://arxiv.org/abs/1810.03611

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Understanding the Origins of Bias in Word Embeddings · HackerPapers discussions

Understanding the Origins of Bias in Word Embeddings — mentioned by mdifrgechd