4 citations · 18 across the 15 of their papers we have counts for
16 papers
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…
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…
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…
Do These LLM Benchmarks Agree? Fixing Benchmark Evaluation with BenchBench
Yotam Perlitz, Ariel Gera, Ofir Arviv +5
Recent advancements in Language Models (LMs) have catalyzed the creation of multiple benchmarks, designed to assess these models' general capabilities. A crucial task, however, is…
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…
Genie: Achieving Human Parity in Content-Grounded Datasets Generation
Asaf Yehudai, Boaz Carmeli, Yosi Mass +5
The lack of high-quality data for content-grounded generation tasks has been identified as a major obstacle to advancing these tasks. To address this gap, we propose Genie, a novel…
Efficient Benchmarking of Language Models
Yotam Perlitz, Elron Bandel, Ariel Gera +6
The increasing versatility of language models (LMs) has given rise to a new class of benchmarks that comprehensively assess a broad range of capabilities. Such benchmarks are assoc…
The Benefits of Bad Advice: Autocontrastive Decoding across Model Layers
Ariel Gera, Roni Friedman, Ofir Arviv +4
Applying language models to natural language processing tasks typically relies on the representations in the final model layer, as intermediate hidden layer representations are pre…
Zero-Shot Text Classification with Self-Training
Ariel Gera, Alon Halfon, Eyal Shnarch +3
Recent advances in large pretrained language models have increased attention to zero-shot text classification. In particular, models finetuned on natural language inference dataset…
Label Sleuth: From Unlabeled Text to a Classifier in a Few Hours
Eyal Shnarch, Alon Halfon, Ariel Gera +19
Text classification can be useful in many real-world scenarios, saving a lot of time for end users. However, building a custom classifier typically requires coding skills and ML kn…
Heuristic-based Inter-training to Improve Few-shot Multi-perspective Dialog Summarization
Benjamin Sznajder, Chulaka Gunasekara, Guy Lev +3
Many organizations require their customer-care agents to manually summarize their conversations with customers. These summaries are vital for decision making purposes of the organi…
Cluster & Tune: Boost Cold Start Performance in Text Classification
Eyal Shnarch, Ariel Gera, Alon Halfon +4
In real-world scenarios, a text classification task often begins with a cold start, when labeled data is scarce. In such cases, the common practice of fine-tuning pre-trained model…
GrASP: A Library for Extracting and Exploring Human-Interpretable Textual Patterns
Piyawat Lertvittayakumjorn, Leshem Choshen, Eyal Shnarch +1
Data exploration is an important step of every data science and machine learning project, including those involving textual data. We provide a novel language tool, in the form of a…
Unsupervised Expressive Rules Provide Explainability and Assist Human Experts Grasping New Domains
Eyal Shnarch, Leshem Choshen, Guy Moshkowich +2
Approaching new data can be quite deterrent; you do not know how your categories of interest are realized in it, commonly, there is no labeled data at hand, and the performance of…
Corpus Wide Argument Mining -- a Working Solution
Liat Ein-Dor, Eyal Shnarch, Lena Dankin +10
One of the main tasks in argument mining is the retrieval of argumentative content pertaining to a given topic. Most previous work addressed this task by retrieving a relatively sm…
Are You Convinced? Choosing the More Convincing Evidence with a Siamese Network
Martin Gleize, Eyal Shnarch, Leshem Choshen +4
With the advancement in argument detection, we suggest to pay more attention to the challenging task of identifying the more convincing arguments. Machines capable of responding an…