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…
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.…
When Can Transformers Count to n?
Gilad Yehudai, Haim Kaplan, Guy Dar +4
Large language models based on the transformer architecture can solve highly complex tasks, yet their fundamental limitations on simple algorithmic problems remain poorly understoo…
Hopping Too Late: Exploring the Limitations of Large Language Models on Multi-Hop Queries
Eden Biran, Daniela Gottesman, Sohee Yang +2
Large language models (LLMs) can solve complex multi-step problems, but little is known about how these computations are implemented internally. Motivated by this, we study how LLM…
In-Context Learning Creates Task Vectors
Roee Hendel, Mor Geva, Amir Globerson
In-context learning (ICL) in Large Language Models (LLMs) has emerged as a powerful new learning paradigm. However, its underlying mechanism is still not well understood. In partic…