Semanlink - Language Models: size AND NLP@Google

An empirical analysis of compute-optimal large language model training

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2023-01-26 About

Characterizing Emergent Phenomena in Large Language Models – Google AI Blog

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2023-01-26 About

Omer Levy sur Twitter : "What if I told you that fine-tuning T5-Large (0.8B params) on a couple hundred examples could outperform GPT-3 (175B params) on a bunch of tasks?"

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2021-10-13 About

[2106.04647] Compacter: Efficient Low-Rank Hypercomplex Adapter Layers

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2021-09-29 About

How Much Knowledge Can You Pack Into the Parameters of a Language Model?

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> It has recently been observed that neural language
models trained on unstructured text can
implicitly store and retrieve knowledge using
natural language queries.

indeed, cf. Facebook's paper [Language Models as Knowledge Bases?](/doc/2019/09/_1909_01066_language_models_as)

> In this short paper,
we measure the practical utility of this
approach by fine-tuning pre-trained models to
answer questions without access to any external
context or knowledge.

> we show that a large language
model pre-trained on unstructured text can
attain competitive results on open-domain question
answering benchmarks without any access
to external knowledge

BUT:

>1. state-of-the-art results only with the largest model
which had 11 billion parameters.
>1. “open-book” models
typically provide some indication of what information
they accessed when answering a question
that provides a useful form of interpretability.
In contrast, our model distributes knowledge
in its parameters in an inexplicable way, which
precludes this form of interpretability.
>1. **the maximum-likelihood objective provides no guarantees as to whether
a model will learn a fact or not.**

So, what's the point? To be compared with this [IBM's paper](/doc/2019/09/_1909_04120_span_selection_pre): "a new pre-training task inspired by reading comprehension and an effort to avoid encoding general knowledge in the transformer network itself"

2020-02-11 About