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Evaluating Image Review Ability of Vision Language Models
Feb 19, 2024
Large-scale vision language models (LVLMs) are language models that are capable of processing images and text inputs by a single model. This paper explores the use of LVLMs to generate review texts for images. The ability of LVLMs to review images is not fully understood, highlighting the need for a methodical evaluation of their review abilities. Unlike image captions, review texts can be written from various perspectives such as image composition and exposure. This diversity of review perspectives makes it difficult to uniquely determine a single correct review for an image. To address this challenge, we introduce an evaluation method based on rank correlation analysis, in which review texts are ranked by humans and LVLMs, then, measures the correlation between these rankings. We further validate this approach by creating a benchmark dataset aimed at assessing the image review ability of recent LVLMs. Our experiments with the dataset reveal that LVLMs, particularly those with proven superiority in other evaluative contexts, excel at distinguishing between high-quality and substandard image reviews.
Japanese Lexical Complexity for Non-Native Readers: A New Dataset
Jun 30, 2023
Lexical complexity prediction (LCP) is the task of predicting the complexity of words in a text on a continuous scale. It plays a vital role in simplifying or annotating complex words to assist readers. To study lexical complexity in Japanese, we construct the first Japanese LCP dataset. Our dataset provides separate complexity scores for Chinese/Korean annotators and others to address the readers' L1-specific needs. In the baseline experiment, we demonstrate the effectiveness of a BERT-based system for Japanese LCP.
Arukikata Travelogue Dataset with Geographic Entity Mention, Coreference, and Link Annotation
May 23, 2023
Geoparsing is a fundamental technique for analyzing geo-entity information in text. We focus on document-level geoparsing, which considers geographic relatedness among geo-entity mentions, and presents a Japanese travelogue dataset designed for evaluating document-level geoparsing systems. Our dataset comprises 200 travelogue documents with rich geo-entity information: 12,171 mentions, 6,339 coreference clusters, and 2,551 geo-entities linked to geo-database entries.