Whisper: robust speech recognition via large-scale weak supervision

This was a paper I presented about in Bang Liu’s research group meeting on 2022-09-30. You can view the slides I used here.

Selective annotation makes language models better few-shot learners

Selective annotation chooses a pool of samples to annotate from a large set of unlabeled data. The main result of the paper is that when this is combined with item-specific prompt retrieval the performance drastically improves (>10% relative gain and lower performance variance). Interestingly, selective annotation does not help for finetuning, or when the prompts are randomly selected. They call their selective annotation method “vote-\(k\)”. selective annotation method permalink Vote-\(k\) essentially creates a network of similaraccording to Sentence-BERT unlabeled instances, and then selects from them with a network importance score that is discounted to promote diversityThe discounting is performed by iteratively adding to the selection set, each time penalizing new nodes for being close to nodes that are already in the selection set.
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Continual-T0: progressively instructing 50+ tasks to language models without forgetting

This was a paper I presented about in Bang Liu’s research group meeting on 2022-06-06. You can view the slides I used here. Continual-T0 (CT0) extends T0 by progressively training it on 8 unseen language generation tasks, while retaining a replay buffer of 1% of the original training data to preserve performance. The result is a model that maintains nearly all of its performance on previous tasks while learning the new tasks.
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Multitask prompted training enables zero-shot task generalization (T0)

T0 builds on T5 by fine-tuning on more natural prompts and testing the model’s generalization to held-out tasks. Compare the training format diagrams for T5 (top) and T0 (bottom): Intuitively, the T0 prompts are more likely to be similar to implicit/explicit prompting that’s present in the pretraining data. The authors created several prompts for each dataset. results permalink Our experiments study two questions. First, does multitask prompted training improve generalization to held-out tasks?
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WordBurner beta

Update 2022-04-27: The beta is over, but the apk is still installable with the instructions below and any feedback sent from inside the app will be received by me. I’m going to be working on this more over the summer, and eventually publishing it on the app store. :) Ever since learning Spanish, it has been a dream of mine to create a vocabulary study app that meets my needs. Duolingo won’t cover advanced vocabulary, Anki requires manually-generated decks, and other apps have expensive subscription plans.
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