These are my notes from research papers I read. Each page’s title is also a link to the abstract or PDF.

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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This was a paper I presented about in Bang Liu’s research group meeting on 2022-04-11. You can view the slides I used here.

QA-GNN: reasoning with language models and knowledge graphs for question answering

This post was created as an assignment in Bang Liu’s IFT6289 course in winter 2022. The structure of the post follows the structure of the assignment: summarization followed by my own comments. The authors create a novel system for combining an LM and a knowledge graph by performing reasoning over a joint graph produced by the LM and the KG, thus solving the problem of irrelevant entities appearing in the knowledge graph and unifying the representations across the LM and KG.
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Experienced well-being rises with income, even above $75,000 per year

Turns out that money does buy happiness. You may have heard that people’s average happiness stops improving once you make more than $75,000/year? Researchers did a better survey with more data and found that that was not the case. The researchers cited 5 methodological improvements over the old research that suggested that it didn’t matter after $75,000: They measured people’s happiness in real time, instead of having people try to remember past happiness levels.
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