Semanlink - NLP@Meta

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NLP@Meta

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5 Documents (Long List)

Meta AI sur Twitter : "New in Nature Human Behavior, Meta AI researchers show how current language models differ from the human brain & highlight the role of long-range & hierarchical predictions..."

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2023-03-03 About

Guillaume Lample sur Twitter : "Today we release LLaMA, 4 foundation models ranging from 7B to 65B parameters..."

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2023-02-25 About

[2212.09741] One Embedder, Any Task: Instruction-Finetuned Text Embeddings

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2023-02-17 About

[2302.04761] Toolformer: Language Models Can Teach Themselves to Use Tools

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> Toolformer, **a model
trained to decide which APIs to call, when to
call them, what arguments to pass, and how to
best incorporate the results into future token
prediction**.

> fulfills the
following desiderata:
> - The use of tools should be learned in a
self-supervised way without requiring large
amounts of human annotations
>- The LM should be able to decide for itself when
and how to use which tool.

> Approach based
on the recent idea of using large LMs with incontext
learning (Brown et al., 2020) to generate
entire datasets from scratch.
>
> Given just a handful of human-written examples
of how an API can be used, 
> - we let a LM annotate
a huge language modeling dataset with potential
API calls. 
> - We then use a self-supervised loss to
determine which of these API calls actually help
the model in predicting future tokens. 
>- Finally, we
finetune the LM itself on the API calls that it considers
useful.

[Jay Hack @mathemagic1an sur twitter](https://twitter.com/mathemagic1an/status/1624870248221663232):

> from a small seed set of human inputs (essentially demonstrating usage of APIs), the training set for this behavior is generated by the LLM itself.
> 
> So what does this mean? We've found a promising way to tightly integrate arbitrary APIs with our best-performing models.

2023-02-13 About

Akari Asai sur Twitter : "Can we solely rely on LLMs’ memories (eg replace search w ChatGPT)? Probably not... Our analysis shows how retrieval is complementary to LLMs’ parametric knowledge..."

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2022-12-21 About

Properties

sl:creationDate : 2023-01-12
sl:creationTime : 2023-01-12T09:19:12Z
rdf:type : sl:Tag
skos:prefLabel : NLP@Meta