4 citations · 12 across the 23 of their papers we have counts for
24 papers
Teaching Values to Machines: Simulating Human-Like Behavior in LLMs
Asaf Yehudai, Naama Rozen, Ariel Gera
Large Language Models (LLMs) demonstrate a remarkable capacity to adopt different personas and roles; however, it remains unclear whether they can manifest behavior that adheres to…
Flash-GMM: A Memory-Efficient Kernel for Scalable Soft Clustering
Gal Bloch, Ariel Gera, Matan Orbach +2
We present \textbf{Flash-GMM}, a fused Triton kernel for efficient computation of Gaussian Mixture Models (GMMs) over large-scale data in a single GPU pass. By eliminating the need…
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…
Robustness as an Emergent Property of Task Performance
Shir Ashury-Tahan, Ariel Gera, Elron Bandel +2
Robustness is widely viewed as a key challenge for real-world applications. However, because current research focuses only on difficult tasks, it partially captures real-world read…
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…
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…
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…
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…
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…
JuStRank: Benchmarking LLM Judges for System Ranking
Ariel Gera, Odellia Boni, Yotam Perlitz +3
Given the rapid progress of generative AI, there is a pressing need to systematically compare and choose between the numerous models and configurations available. The scale and ver…
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…
Unitxt: Flexible, Shareable and Reusable Data Preparation and Evaluation for Generative AI
Elron Bandel, Yotam Perlitz, Elad Venezian +9
In the dynamic landscape of generative NLP, traditional text processing pipelines limit research flexibility and reproducibility, as they are tailored to specific dataset, task, an…
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…
Active Learning for Natural Language Generation
Yotam Perlitz, Ariel Gera, Michal Shmueli-Scheuer +3
The field of Natural Language Generation (NLG) suffers from a severe shortage of labeled data due to the extremely expensive and time-consuming process involved in manual annotatio…
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…
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…
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…
Financial Event Extraction Using Wikipedia-Based Weak Supervision
Liat Ein-Dor, Ariel Gera, Orith Toledo-Ronen +6
Extraction of financial and economic events from text has previously been done mostly using rule-based methods, with more recent works employing machine learning techniques. This w…
A Dataset of General-Purpose Rebuttal
Matan Orbach, Yonatan Bilu, Ariel Gera +8
In Natural Language Understanding, the task of response generation is usually focused on responses to short texts, such as tweets or a turn in a dialog. Here we present a novel tas…
Argument Invention from First Principles
Yonatan Bilu, Ariel Gera, Daniel Hershcovich +6
Competitive debaters often find themselves facing a challenging task -- how to debate a topic they know very little about, with only minutes to prepare, and without access to books…
Controversy in Context
Benjamin Sznajder, Ariel Gera, Yonatan Bilu +5
With the growing interest in social applications of Natural Language Processing and Computational Argumentation, a natural question is how controversial a given concept is. Prior w…