Semanlink - Memory in deep learning ; Transformers AND Language Model

Memory in deep learning ; Transformers AND Language Model

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4 Documents

Allen Institute for AI sur Twitter : "MemPrompt, appearing at #EMNLP2022, is a new way to "fix" #GPT3 after deployment via user interaction"

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2022-12-11 About

Guillaume Lample sur Twitter : "Last year, we showed that you can outperform a 24-layer transformer in language modeling with just...

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2020-10-10 About

[2004.07202] Entities as Experts: Sparse Memory Access with Entity Supervision

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2020-07-11 About

[1909.04120] Span Selection Pre-training for Question Answering

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> a **new pre-training task inspired by reading
comprehension** and an **effort to avoid encoding general knowledge in the transformer network itself**

Current transformer architectures store general knowledge -> large models, long pre-training time. Better to offload the requirement of general knowledge to a sparsely activated network.

"Span selection" as an additional auxiliary task: the query is a sentence drawn from a corpus
with a term replaced with a special token: [BLANK]. The term replaced by the blank is the answer term. The passage is
relevant as determined by a BM25 search, and answer-bearing (containing the answer
term). Unlike BERT’s cloze task, where the answer must be drawn from the model itself, the answer is found in a passage
using language understanding.

> **We hope to progress to a model of general purpose language modeling that uses an indexed long
term memory to retrieve world knowledge, rather than holding it in the densely activated transformer encoder layers.**

2019-09-18 About