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WildIFEval: Instruction Following in the Wild
Gili Lior, Asaf Yehudai, Ariel Gera +1
Recent LLMs have shown remarkable success in following user instructions, yet handling instructions with multiple constraints remains a significant challenge. In this work, we intr…
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…
Guided Query Refinement: Multimodal Hybrid Retrieval with Test-Time Optimization
Omri Uzan, Asaf Yehudai, Roi pony +2
Multimodal encoders have pushed the boundaries of visual document retrieval, matching textual query tokens directly to image patches and achieving state-of-the-art performance on p…
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…
An Analysis of Hyper-Parameter Optimization Methods for Retrieval Augmented Generation
Matan Orbach, Ohad Eytan, Benjamin Sznajder +12
Optimizing Retrieval-Augmented Generation (RAG) configurations for specific tasks is a complex and resource-intensive challenge. Motivated by this challenge, frameworks for RAG hyp…
Debatable Intelligence: Benchmarking LLM Judges via Debate Speech Evaluation
Noy Sternlicht, Ariel Gera, Roy Bar-Haim +2
We introduce Debate Speech Evaluation as a novel and challenging benchmark for assessing LLM judges. Evaluating debate speeches requires a deep understanding of the speech at multi…