65 citations · 208 across the 57 of their papers we have counts for
6 papers · 1 filter
Mismatch Quest: Visual and Textual Feedback for Image-Text Misalignment
Brian Gordon, Yonatan Bitton, Yonatan Shafir +5
While existing image-text alignment models reach high quality binary assessments, they fall short of pinpointing the exact source of misalignment. In this paper, we present a metho…
VideoCon: Robust Video-Language Alignment via Contrast Captions
Hritik Bansal, Yonatan Bitton, Idan Szpektor +2
Despite being (pre)trained on a massive amount of data, state-of-the-art video-language alignment models are not robust to semantically-plausible contrastive changes in the video c…
Factually Consistent Summarization via Reinforcement Learning with Textual Entailment Feedback
Paul Roit, Johan Ferret, Lior Shani +16
Despite the seeming success of contemporary grounded text generation systems, they often tend to generate factually inconsistent text with respect to their input. This phenomenon i…
Transferring Visual Attributes from Natural Language to Verified Image Generation
Rodrigo Valerio, Joao Bordalo, Michal Yarom +3
Text to image generation methods (T2I) are widely popular in generating art and other creative artifacts. While visual hallucinations can be a positive factor in scenarios where cr…
TrueTeacher: Learning Factual Consistency Evaluation with Large Language Models
Zorik Gekhman, Jonathan Herzig, Roee Aharoni +2
Factual consistency evaluation is often conducted using Natural Language Inference (NLI) models, yet these models exhibit limited success in evaluating summaries. Previous work imp…
What You See is What You Read? Improving Text-Image Alignment Evaluation
Michal Yarom, Yonatan Bitton, Soravit Changpinyo +5
Automatically determining whether a text and a corresponding image are semantically aligned is a significant challenge for vision-language models, with applications in generative t…