4 citations · 9 across the 17 of their papers we have counts for
5 papers · 1 filter
LMs as Task-Specific Knowledge Bases: An Interpretability Analysis
Amit Elhelo, Amir Globerson, Mor Geva
Language models (LMs) capture large amounts of factual knowledge applicable to a wide range of tasks, motivating the view of their parameters as a knowledge base. An important prop…
Cost-Aware Learning
Clara Mohri, Amir Globerson, Haim Kaplan +2
We consider the problem of Cost-Aware Learning, where sampling different components of a finite-sum objective incurs different costs. The objective is to reach a target error while…
Lost in Tokenization: Fundamental Trade-offs in Graph Tokenization for Transformers
Maya Bechler-Speicher, Gilad Yehudai, Gil Harari +3
Transformers have become a central architecture for graph learning, but their application to graphs requires first choosing a tokenization: a graph-to-token map that determines whi…
Controllable User Simulation
Guy Tennenholtz, Ofer Meshi, Amir Globerson +3
Using offline datasets to evaluate conversational agents often fails to cover rare scenarios or to support testing new policies. This has motivated the use of controllable user sim…
Latent Reasoning with Supervised Thinking States
Ido Amos, Avi Caciularu, Mor Geva +4
Reasoning with a chain-of-thought (CoT) enables Large Language Models (LLMs) to solve complex tasks but incurs significant inference costs due to the generation of long rationales.…