What is recorded here?
Generated and filtered API-call annotations by their effect on language-model prediction loss, then trained a model to select and use external tools.
Source-linked catalogue note. A detailed explanatory guide has not yet been prepared for this entry.
Scientific scope
The original method requires a few demonstrations and evaluates specified APIs. Its paper notes limits on chained and interactive tool use; no general autonomy or correctness guarantee is implied.
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People and systems
Timo Schick; Jane Dwivedi-Yu; Roberto Dessì; Roberta Raileanu; Maria Lomeli; Eric Hambro; Luke Zettlemoyer; Nicola Cancedda; Thomas Scialom
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.
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English catalogue narrative
Schick and colleagues: Toolformer self-supervised API learning
Generated and filtered API-call annotations by their effect on language-model prediction loss, then trained a model to select and use external tools.
Scientific scope
The original method requires a few demonstrations and evaluates specified APIs. Its paper notes limits on chained and interactive tool use; no general autonomy or correctness guarantee is implied.
Reading status
This is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.
People and systems
Timo Schick; Jane Dwivedi-Yu; Roberto Dessì; Roberta Raileanu; Maria Lomeli; Eric Hambro; Luke Zettlemoyer; Nicola Cancedda; Thomas Scialom
Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.