340 research outputs found
Table and Image Generation for Investigating Knowledge of Entities in Pre-trained Vision and Language Models
In this paper, we propose a table and image generation task to verify how the
knowledge about entities acquired from natural language is retained in Vision &
Language (V & L) models. This task consists of two parts: the first is to
generate a table containing knowledge about an entity and its related image,
and the second is to generate an image from an entity with a caption and a
table containing related knowledge of the entity. In both tasks, the model must
know the entities used to perform the generation properly. We created the
Wikipedia Table and Image Generation (WikiTIG) dataset from about 200,000
infoboxes in English Wikipedia articles to perform the proposed tasks. We
evaluated the performance on the tasks with respect to the above research
question using the V & L model OFA, which has achieved state-of-the-art results
in multiple tasks. Experimental results show that OFA forgets part of its
entity knowledge by pre-training as a complement to improve the performance of
image related tasks.Comment: Accepted at ACL 202
Artwork Explanation in Large-scale Vision Language Models
Large-scale vision-language models (LVLMs) output text from images and
instructions, demonstrating advanced capabilities in text generation and
comprehension. However, it has not been clarified to what extent LVLMs
understand the knowledge necessary for explaining images, the complex
relationships between various pieces of knowledge, and how they integrate these
understandings into their explanations. To address this issue, we propose a new
task: the artwork explanation generation task, along with its evaluation
dataset and metric for quantitatively assessing the understanding and
utilization of knowledge about artworks. This task is apt for image description
based on the premise that LVLMs are expected to have pre-existing knowledge of
artworks, which are often subjects of wide recognition and documented
information. It consists of two parts: generating explanations from both images
and titles of artworks, and generating explanations using only images, thus
evaluating the LVLMs' language-based and vision-based knowledge. Alongside, we
release a training dataset for LVLMs to learn explanations that incorporate
knowledge about artworks. Our findings indicate that LVLMs not only struggle
with integrating language and visual information but also exhibit a more
pronounced limitation in acquiring knowledge from images alone. The datasets
(ExpArt=Explain Artworks) are available at
https://huggingface.co/datasets/naist-nlp/ExpArt
Does Pre-trained Language Model Actually Infer Unseen Links in Knowledge Graph Completion?
Knowledge graphs (KGs) consist of links that describe relationships between
entities. Due to the difficulty of manually enumerating all relationships
between entities, automatically completing them is essential for KGs. Knowledge
Graph Completion (KGC) is a task that infers unseen relationships between
entities in a KG. Traditional embedding-based KGC methods, such as RESCAL,
TransE, DistMult, ComplEx, RotatE, HAKE, HousE, etc., infer missing links using
only the knowledge from training data. In contrast, the recent Pre-trained
Language Model (PLM)-based KGC utilizes knowledge obtained during pre-training.
Therefore, PLM-based KGC can estimate missing links between entities by reusing
memorized knowledge from pre-training without inference. This approach is
problematic because building KGC models aims to infer unseen links between
entities. However, conventional evaluations in KGC do not consider inference
and memorization abilities separately. Thus, a PLM-based KGC method, which
achieves high performance in current KGC evaluations, may be ineffective in
practical applications. To address this issue, we analyze whether PLM-based KGC
methods make inferences or merely access memorized knowledge. For this purpose,
we propose a method for constructing synthetic datasets specified in this
analysis and conclude that PLMs acquire the inference abilities required for
KGC through pre-training, even though the performance improvements mostly come
from textual information of entities and relations.Comment: 15 pages, 10 figure
Model-based Subsampling for Knowledge Graph Completion
Subsampling is effective in Knowledge Graph Embedding (KGE) for reducing
overfitting caused by the sparsity in Knowledge Graph (KG) datasets. However,
current subsampling approaches consider only frequencies of queries that
consist of entities and their relations. Thus, the existing subsampling
potentially underestimates the appearance probabilities of infrequent queries
even if the frequencies of their entities or relations are high. To address
this problem, we propose Model-based Subsampling (MBS) and Mixed Subsampling
(MIX) to estimate their appearance probabilities through predictions of KGE
models. Evaluation results on datasets FB15k-237, WN18RR, and YAGO3-10 showed
that our proposed subsampling methods actually improved the KG completion
performances for popular KGE models, RotatE, TransE, HAKE, ComplEx, and
DistMult.Comment: Accepted by AACL 2023; 9 pages, 3 figures, 5 table
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