261 citations · 332 across the 12 of their papers we have counts for
12 papers
When Does In-Context Search Help? A Sampling-Complexity Theory of Reflection-Driven Reasoning
Yotam Wolf, Noam Wies, Amnon Shashua
Training large language models (LLMs) with extended reasoning has enabled in-context search, in which models iteratively generate, critique, and revise solution attempts. We provid…
FormulaOne: Measuring the Depth of Algorithmic Reasoning Beyond Competitive Programming
Gal Beniamini, Yuval Dor, Alon Vinnikov +10
Frontier AI models demonstrate formidable breadth of knowledge. But how close are they to true human -- or superhuman -- expertise? Genuine experts can tackle the hardest problems…
Artificial Expert Intelligence through PAC-reasoning
Shai Shalev-Shwartz, Amnon Shashua, Gal Beniamini +6
Artificial Expert Intelligence (AEI) seeks to transcend the limitations of both Artificial General Intelligence (AGI) and narrow AI by integrating domain-specific expertise with cr…
Tradeoffs Between Alignment and Helpfulness in Language Models with Steering Methods
Yotam Wolf, Noam Wies, Dorin Shteyman +3
Language model alignment has become an important component of AI safety, allowing safe interactions between humans and language models, by enhancing desired behaviors and inhibitin…
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…
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…
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…
Sub-Task Decomposition Enables Learning in Sequence to Sequence Tasks
Noam Wies, Yoav Levine, Amnon Shashua
The field of Natural Language Processing has experienced a dramatic leap in capabilities with the recent introduction of huge Language Models. Despite this success, natural languag…
The Inductive Bias of In-Context Learning: Rethinking Pretraining Example Design
Yoav Levine, Noam Wies, Daniel Jannai +3
Pretraining Neural Language Models (NLMs) over a large corpus involves chunking the text into training examples, which are contiguous text segments of sizes processable by the neur…
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…
Deep autoregressive models for the efficient variational simulation of many-body quantum systems
Or Sharir, Yoav Levine, Noam Wies +2
Artificial Neural Networks were recently shown to be an efficient representation of highly-entangled many-body quantum states. In practical applications, neural-network states inhe…