261 citations · 332 across the 12 of their papers we have counts for
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cs.CL2023★ 1 cited
Align With Purpose: Optimize Desired Properties in CTC Models with a General Plug-and-Play Framework
Eliya Segev, Maya Alroy, Ronen Katsir +8
Connectionist Temporal Classification (CTC) is a widely used criterion for training supervised sequence-to-sequence (seq2seq) models. It enables learning the relations between inpu…
cs.CL2023★ 45 cited
Fundamental Limitations of Alignment in Large Language Models
Yotam Wolf, Noam Wies, Oshri Avnery +2
An important aspect in developing language models that interact with humans is aligning their behavior to be useful and unharmful for their human users. This is usually achieved by…
cs.CL2023★ 8 cited
The Learnability of In-Context Learning
Noam Wies, Yoav Levine, Amnon Shashua
In-context learning is a surprising and important phenomenon that emerged when modern language models were scaled to billions of learned parameters. Without modifying a large langu…