6 citations · 16 across the 27 of their papers we have counts for
10 papers · 1 filter
SAUCE: Synchronous and Asynchronous User-Customizable Environment for Multi-Agent LLM Interaction
Shlomo Neuberger, Niv Eckhaus, Uri Berger +3
Many human interactions, such as political debates, are carried out in group settings, where there are arbitrarily many participants, each with different views and agendas. To expl…
Looking Beyond The Top-1: Transformers Determine Top Tokens In Order
Daria Lioubashevski, Tomer Schlank, Gabriel Stanovsky +1
Understanding the inner workings of Transformers is crucial for achieving more accurate and efficient predictions. In this work, we analyze the computation performed by Transformer…
Surveying the Landscape of Image Captioning Evaluation: A Comprehensive Taxonomy, Trends and Metrics Analysis
Uri Berger, Gabriel Stanovsky, Omri Abend +1
The task of image captioning has recently been gaining popularity, and with it the complex task of evaluating the quality of image captioning models. In this work, we present the f…
SEAM: A Stochastic Benchmark for Multi-Document Tasks
Gili Lior, Avi Caciularu, Arie Cattan +3
Various tasks, such as summarization, multi-hop question answering, or coreference resolution, are naturally phrased over collections of real-world documents. Such tasks present a…
Applying Intrinsic Debiasing on Downstream Tasks: Challenges and Considerations for Machine Translation
Bar Iluz, Yanai Elazar, Asaf Yehudai +1
Most works on gender bias focus on intrinsic bias -- removing traces of information about a protected group from the model's internal representation. However, these works are often…
In-Context Learning on a Budget: A Case Study in Token Classification
Uri Berger, Tal Baumel, Gabriel Stanovsky
Few shot in-context learning (ICL) typically assumes access to large annotated training sets. However, in many real world scenarios, such as domain adaptation, there is only a limi…
A Nurse is Blue and Elephant is Rugby: Cross Domain Alignment in Large Language Models Reveal Human-like Patterns
Asaf Yehudai, Taelin Karidi, Gabriel Stanovsky +2
Cross-domain alignment refers to the task of mapping a concept from one domain to another. For example, ``If a \textit{doctor} were a \textit{color}, what color would it be?''. Thi…
Do Zombies Understand? A Choose-Your-Own-Adventure Exploration of Machine Cognition
Ariel Goldstein, Gabriel Stanovsky
Recent advances in LLMs have sparked a debate on whether they understand text. In this position paper, we argue that opponents in this debate hold different definitions for underst…
Leveraging Collection-Wide Similarities for Unsupervised Document Structure Extraction
Gili Lior, Yoav Goldberg, Gabriel Stanovsky
Document collections of various domains, e.g., legal, medical, or financial, often share some underlying collection-wide structure, which captures information that can aid both hum…
State of What Art? A Call for Multi-Prompt LLM Evaluation
Moran Mizrahi, Guy Kaplan, Dan Malkin +3
Recent advances in large language models (LLMs) have led to the development of various evaluation benchmarks. These benchmarks typically rely on a single instruction template for e…