96 citations · 260 across the 21 of their papers we have counts for
27 papers
Can LLMs Help Uncover Insights about LLMs? A Large-Scale, Evolving Literature Analysis of Frontier LLMs
Jungsoo Park, Junmo Kang, Gabriel Stanovsky +1
The surge of LLM studies makes synthesizing their findings challenging. Analysis of experimental results from literature can uncover important trends across studies, but the time-c…
K-QA: A Real-World Medical Q&A Benchmark
Itay Manes, Naama Ronn, David Cohen +3
Ensuring the accuracy of responses provided by large language models (LLMs) is crucial, particularly in clinical settings where incorrect information may directly impact patient he…
Exploring the Impact of Training Data Distribution and Subword Tokenization on Gender Bias in Machine Translation
Bar Iluz, Tomasz Limisiewicz, Gabriel Stanovsky +1
We study the effect of tokenization on gender bias in machine translation, an aspect that has been largely overlooked in previous works. Specifically, we focus on the interactions…
Schema-Driven Information Extraction from Heterogeneous Tables
Fan Bai, Junmo Kang, Gabriel Stanovsky +3
In this paper, we explore the question of whether large language models can support cost-efficient information extraction from tables. We introduce schema-driven information extrac…
"Covid vaccine is against Covid but Oxford vaccine is made at Oxford!" Semantic Interpretation of Proper Noun Compounds
Keshav Kolluru, Gabriel Stanovsky, Mausam
Proper noun compounds, e.g., "Covid vaccine", convey information in a succinct manner (a "Covid vaccine" is a "vaccine that immunizes against the Covid disease"). These are commonl…
A Computational Acquisition Model for Multimodal Word Categorization
Uri Berger, Gabriel Stanovsky, Omri Abend +1
Recent advances in self-supervised modeling of text and images open new opportunities for computational models of child language acquisition, which is believed to rely heavily on c…