8 citations · 9 across the 6 of their papers we have counts for
6 papers
Linguistic and Argument Diversity in Synthetic Data for Function-Calling Agents
Dan Greenstein, Zohar Karnin, Chen Amiraz +1
The construction of function calling agents has emerged as a promising avenue for extending model capabilities. A major challenge for this task is obtaining high quality diverse da…
The Cross-Lingual Cost: Retrieval Biases in RAG over Arabic-English Corpora
Chen Amiraz, Yaroslav Fyodorov, Elad Haramaty +2
Cross-lingual retrieval-augmented generation (RAG) is a critical capability for retrieving and generating answers across languages. Prior work in this context has mostly focused on…
The Distracting Effect: Understanding Irrelevant Passages in RAG
Chen Amiraz, Florin Cuconasu, Simone Filice +1
A well-known issue with Retrieval Augmented Generation (RAG) is that retrieved passages that are irrelevant to the query sometimes distract the answer-generating LLM, causing it to…
Recovery Guarantees for Distributed-OMP
Chen Amiraz, Robert Krauthgamer, Boaz Nadler
We study distributed schemes for high-dimensional sparse linear regression, based on orthogonal matching pursuit (OMP). Such schemes are particularly suited for settings where a ce…
Distributed Sparse Normal Means Estimation with Sublinear Communication
Chen Amiraz, Robert Krauthgamer, Boaz Nadler
We consider the problem of sparse normal means estimation in a distributed setting with communication constraints. We assume there are machines, each holding -dimensional ob…
Tight Recovery Guarantees for Orthogonal Matching Pursuit Under Gaussian Noise
Chen Amiraz, Robert Krauthgamer, Boaz Nadler
Orthogonal Matching pursuit (OMP) is a popular algorithm to estimate an unknown sparse vector from multiple linear measurements of it. Assuming exact sparsity and that the measurem…