Revisiting Biased Word Embeddings

Revisiting Biased Word Embeddings

The topic of bias in word embeddings gets yet another pass this week. It all started a few years ago, when an analogy task performed on Word2Vec embeddings showed some indications of gender bias around professions (as well as other forms of social bias getting reproduced in the algorithm’s embeddings). We covered the topic again a while later, covering methods for de-biasing embeddings to counteract this effect. And now we’re back, with a second pass on the original Word2Vec analogy task, but where the researchers deconstructed the “rules” of the analogies themselves and came to an interesting discovery: the bias seems to be, at least in part, an artifact of the analogy construction method. Intrigued? So were we… Relevant link: https://arxiv.org/abs/1905.09866

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Episoder(321)

Understanding AI Text Watermarking

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A Scientific Deep Dive into Overconfident LLMs: Interview with Kaitlyn Zhou (Cornell)

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Reasoning Models: When LLMs Went Beyond Fancy Autocomplete

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3 Aug 25min

Distillation, or, How to Steal a Model

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27 Jul 23min

Invisible LLM Failures and AI Fluency with Chris Potts (Stanford)

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20 Jul 41min

Still summer break: back next week

Still summer break: back next week

Still summer break: back next week by Katie Malone

13 Jul 25s

Summer break: back soon

Summer break: back soon

Summer break: back soon by Katie Malone

6 Jul 36s

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