9 citations · 9 across the 4 of their papers we have counts for
4 papers
Co-LMLM: Continuous-Query Limited Memory Language Models
Yair Feldman, Linxi Zhao, Nathan Godey +5
Externalizing knowledge in LLM pre-training is a promising avenue to achieve higher performance at smaller scales, control knowledge use, and overall increase model transparency. W…
No Mean Feat: Simple, Strong Baselines for Context Compression
Yair Feldman, Yoav Artzi
Context compression reduces Transformer inference costs by replacing lengthy inputs with shorter pre-computed representations. It carries significant benefits for retrieval-augment…
Breadcrumbs Reasoning: Memory-Efficient Reasoning with Compression Beacons
Giovanni Monea, Yair Feldman, Shankar Padmanabhan +2
The scalability of large language models for long-context reasoning is severely constrained by the linear growth of their Transformer key-value cache, which incurs significant memo…
Multi-Hop Paragraph Retrieval for Open-Domain Question Answering
Yair Feldman, Ran El-Yaniv
This paper is concerned with the task of multi-hop open-domain Question Answering (QA). This task is particularly challenging since it requires the simultaneous performance of text…