1 citations · 1 across the 4 of their papers we have counts for
6 papers
Task-Adaptive Embedding Refinement via Test-time LLM Guidance
Ariel Gera, Shir Ashury-Tahan, Gal Bloch +2
We explore the effectiveness of an LLM-guided query refinement paradigm for extending the usability of embedding models to challenging zero-shot search and classification tasks. Ou…
ErrorMap and ErrorAtlas: Charting the Failure Landscape of Large Language Models
Shir Ashury-Tahan, Yifan Mai, Elron Bandel +2
Large Language Models (LLM) benchmarks tell us when models fail, but not why they fail. A wrong answer on a reasoning dataset may stem from formatting issues, calculation errors, o…
The Mighty ToRR: A Benchmark for Table Reasoning and Robustness
Shir Ashury-Tahan, Yifan Mai, Rajmohan C +8
Despite its real-world significance, model performance on tabular data remains underexplored, leaving uncertainty about which model to rely on and which prompt configuration to ado…
Robustness as an Emergent Property of Task Performance
Shir Ashury-Tahan, Ariel Gera, Elron Bandel +2
Robustness is often regarded as a critical future challenge for real-world applications, where stability is essential. However, as models often learn tasks in a similar order, we h…
Data-driven Coreference-based Ontology Building
Shir Ashury-Tahan, Amir David Nissan Cohen, Nadav Cohen +2
While coreference resolution is traditionally used as a component in individual document understanding, in this work we take a more global view and explore what can we learn about…
Label-Efficient Model Selection for Text Generation
Shir Ashury-Tahan, Ariel Gera, Benjamin Sznajder +3
Model selection for a given target task can be costly, as it may entail extensive annotation of the quality of outputs of different models. We introduce DiffUse, an efficient metho…