90 citations · 421 across the 24 of their papers we have counts for
49 papers · 1 filter
Training Vision-Language Models with Less Bimodal Supervision
Elad Segal, Ben Bogin, Jonathan Berant
Standard practice in pretraining multimodal models, such as vision-language models, is to rely on pairs of aligned inputs from both modalities, for example, aligned image-text pair…
CommonsenseQA 2.0: Exposing the Limits of AI through Gamification
Alon Talmor, Ori Yoran, Ronan Le Bras +4
Constructing benchmarks that test the abilities of modern natural language understanding models is difficult - pre-trained language models exploit artifacts in benchmarks to achiev…
COVR: A test-bed for Visually Grounded Compositional Generalization with real images
Ben Bogin, Shivanshu Gupta, Matt Gardner +1
While interest in models that generalize at test time to new compositions has risen in recent years, benchmarks in the visually-grounded domain have thus far been restricted to syn…
Finding needles in a haystack: Sampling Structurally-diverse Training Sets from Synthetic Data for Compositional Generalization
Inbar Oren, Jonathan Herzig, Jonathan Berant
Modern semantic parsers suffer from two principal limitations. First, training requires expensive collection of utterance-program pairs. Second, semantic parsers fail to generalize…
Turning Tables: Generating Examples from Semi-structured Tables for Endowing Language Models with Reasoning Skills
Ori Yoran, Alon Talmor, Jonathan Berant
Models pre-trained with a language modeling objective possess ample world knowledge and language skills, but are known to struggle in tasks that require reasoning. In this work, we…
Break, Perturb, Build: Automatic Perturbation of Reasoning Paths Through Question Decomposition
Mor Geva, Tomer Wolfson, Jonathan Berant
Recent efforts to create challenge benchmarks that test the abilities of natural language understanding models have largely depended on human annotations. In this work, we introduc…