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20142023
most citedOn the Opportunities and Risks of Foundation Models

2.3k citations · 4.2k across the 44 of their papers we have counts for

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Showing cs.CLShow all

6 papers · 1 filter

cs.CL2023★ 13 cited

Focused Transformer: Contrastive Training for Context Scaling

Szymon Tworkowski, Konrad Staniszewski, Mikołaj Pacek +3

Large language models have an exceptional capability to incorporate new information in a contextual manner. However, the full potential of such an approach is often restrained due…

cs.CL2023

Lexinvariant Language Models

Qian Huang, Eric Zelikman, Sarah Li Chen +3

Token embeddings, a mapping from discrete lexical symbols to continuous vectors, are at the heart of any language model (LM). However, lexical symbol meanings can also be determine…

cs.CL2023★ 168 cited

PaLM 2 Technical Report

Rohan Anil, Andrew M. Dai, Orhan Firat +125

We introduce PaLM 2, a new state-of-the-art language model that has better multilingual and reasoning capabilities and is more compute-efficient than its predecessor PaLM. PaLM 2 i…

cs.CL2022★ 22 cited

Language Model Cascades

David Dohan, Winnie Xu, Aitor Lewkowycz +9

Prompted models have demonstrated impressive few-shot learning abilities. Repeated interactions at test-time with a single model, or the composition of multiple models together, fu…

cs.CL2022★ 45 cited

Exploring Length Generalization in Large Language Models

Cem Anil, Yuhuai Wu, Anders Andreassen +7

The ability to extrapolate from short problem instances to longer ones is an important form of out-of-distribution generalization in reasoning tasks, and is crucial when learning f…

cs.CL2022★ 281 cited

Solving Quantitative Reasoning Problems with Language Models

Aitor Lewkowycz, Anders Andreassen, David Dohan +11

Language models have achieved remarkable performance on a wide range of tasks that require natural language understanding. Nevertheless, state-of-the-art models have generally stru…