45 citations · 127 across the 6 of their papers we have counts for
13 papers
MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning
Ehud Karpas, Omri Abend, Yonatan Belinkov +14
Huge language models (LMs) have ushered in a new era for AI, serving as a gateway to natural-language-based knowledge tasks. Although an essential element of modern AI, LMs are als…
Standing on the Shoulders of Giant Frozen Language Models
Yoav Levine, Itay Dalmedigos, Ori Ram +10
Huge pretrained language models (LMs) have demonstrated surprisingly good zero-shot capabilities on a wide variety of tasks. This gives rise to the appealing vision of a single, ve…
Which transformer architecture fits my data? A vocabulary bottleneck in self-attention
Noam Wies, Yoav Levine, Daniel Jannai +1
After their successful debut in natural language processing, Transformer architectures are now becoming the de-facto standard in many domains. An obstacle for their deployment over…
The Depth-to-Width Interplay in Self-Attention
Yoav Levine, Noam Wies, Or Sharir +2
Self-attention architectures, which are rapidly pushing the frontier in natural language processing, demonstrate a surprising depth-inefficient behavior: previous works indicate th…
On the Ethics of Building AI in a Responsible Manner
Shai Shalev-Shwartz, Shaked Shammah, Amnon Shashua
The AI-alignment problem arises when there is a discrepancy between the goals that a human designer specifies to an AI learner and a potential catastrophic outcome that does not re…
SenseBERT: Driving Some Sense into BERT
Yoav Levine, Barak Lenz, Or Dagan +6
The ability to learn from large unlabeled corpora has allowed neural language models to advance the frontier in natural language understanding. However, existing self-supervision t…