activity
20222024
most citedToolformer: Language Models Can Teach Themselves to Use Tools

400 citations · 744 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2024

TOOLVERIFIER: Generalization to New Tools via Self-Verification

Dheeraj Mekala, Jason Weston, Jack Lanchantin +4

Teaching language models to use tools is an important milestone towards building general assistants, but remains an open problem. While there has been significant progress on learn…

cs.CL2023143 cited

Augmented Language Models: a Survey

Grégoire Mialon, Roberto Dessì, Maria Lomeli +10

This survey reviews works in which language models (LMs) are augmented with reasoning skills and the ability to use tools. The former is defined as decomposing a potentially comple…

cs.CL2023400 cited

Toolformer: Language Models Can Teach Themselves to Use Tools

Timo Schick, Jane Dwivedi-Yu, Roberto Dessì +5

Language models (LMs) exhibit remarkable abilities to solve new tasks from just a few examples or textual instructions, especially at scale. They also, paradoxically, struggle with…

cs.CL2022201 cited

Atlas: Few-shot Learning with Retrieval Augmented Language Models

Gautier Izacard, Patrick Lewis, Maria Lomeli +7

Large language models have shown impressive few-shot results on a wide range of tasks. However, when knowledge is key for such results, as is the case for tasks such as question an…

cs.IR2022

Improving Wikipedia Verifiability with AI

Fabio Petroni, Samuel Broscheit, Aleksandra Piktus +10

Verifiability is a core content policy of Wikipedia: claims that are likely to be challenged need to be backed by citations. There are millions of articles available online and tho…