2 citations · 10 across the 59 of their papers we have counts for
62 papers
IMFD: End-to-end Multi-Face Forgery Detection through Instruction-based Large Vision-Language Models
Dasom Choi, Sangjun Moon, Hyeongchan Im +5
The rapid increase of deepfakes has raised significant concerns due to their spread on social media. Traditional multi-face forgery detectors crop and verify each face independentl…
Overfitting Mitigation via Singular Value Decomposition in Minimum Bayes Risk Decoding
Riza Setiawan Soetedjo, Yusuke Sakai, Hidetaka Kamigaito +2
Minimum Bayes Risk (MBR) decoding enables high-quality text generation by selecting the hypothesis that maximizes a utility metric over sampled pseudo-references. However, it is hi…
ExpArt-KG: Artwork Image Description Generation through Iterative Exploration of Knowledge Graphs
Yuta Kato, Shintaro Ozaki, Kazuki Hayashi +4
Large Vision-Language Models (LVLMs) achieve strong performance on image-grounded text generation and visual question answering. However, it remains difficult for them to comprehen…
Simile Understanding in Text-to-Image Models: An Evaluation Framework
Luecheng Wang, Shintaro Ozaki, Hidetaka Kamigaito +4
Similes provide a compact and expressive way to describe visual characteristics in text prompts. Recent text-to-image models (t2i models) can produce visually compelling outputs fr…
Noisy-Channel Minimum Bayes Risk Decoding
Yusuke Sakai, Hidetaka Kamigaito, Taro Watanabe
Minimum Bayes Risk (MBR) decoding yields more robust and higher-quality text generation than maximum a posteriori (MAP) decoding by selecting hypotheses that maximize expected util…
Multilinguality of Large Language Models From a Structural Perspective
Haruki Sakajo, Yusuke Sakai, Hidetaka Kamigaito +1
Large language models (LLMs) have excelled in processing multiple languages through pre- and post-training on multilingual data, even though English dominates the training data. Pr…
Enhancing Factuality through Consensus and Consistency in Summarization Using Minimum Bayes Risk Decoding
Riza Setiawan Soetedjo, Yusuke Sakai, Hidetaka Kamigaito +3
Improving the quality of model-generated summaries, especially factuality, the accuracy of a summary with respect to its source content, remains a challenge. While reranking could…
CArtBench: Evaluating Vision-Language Models on Chinese Art Understanding, Interpretation, and Authenticity
Xuefeng Wei, Zhixuan Wang, Xuan Zhou +5
We introduce CARTBENCH, a museum-grounded benchmark for evaluating vision-language models (VLMs) on Chinese artworks beyond short-form recognition and QA. CARTBENCH comprises four…
Edit-level Majority Voting Mitigates Over-Correction in LLM-based Grammatical Error Correction
Takumi Goto, Yusuke Sakai, Taro Watanabe
Grammatical error correction using large language models often suffers from the over-correction issue. To mitigate this, we propose a training-free inference method that performs e…
HalluCiteChecker: A Lightweight Toolkit for Hallucinated Citation Detection and Verification in the Era of AI Scientists
Yusuke Sakai, Hidetaka Kamigaito, Taro Watanabe
We introduce HalluCiteChecker, a toolkit for detecting and verifying hallucinated citations in scientific papers. While AI assistant technologies have transformed the academic writ…
Revisiting Non-Verbatim Memorization in Large Language Models: The Role of Entity Surface Forms
Yuto Nishida, Naoki Shikoda, Yosuke Kishinami +4
Understanding what kinds of factual knowledge large language models (LLMs) memorize is essential for evaluating their reliability and limitations. Entity-based QA is a common frame…
XQ-MEval: A Dataset with Cross-lingual Parallel Quality for Benchmarking Translation Metrics
Jingxuan Liu, Zhi Qu, Jin Tei +3
Automatic evaluation metrics are essential for building multilingual translation systems. The common practice of evaluating these systems is averaging metric scores across language…
Identifying Influential N-grams in Confidence Calibration via Regression Analysis
Shintaro Ozaki, Wataru Hashimoto, Hidetaka Kamigaito +2
While large language models (LLMs) improve performance by explicit reasoning, their responses are often overconfident, even though they include linguistic expressions demonstrating…
StructLens: A Structural Lens for Language Models via Maximum Spanning Trees
Haruki Sakajo, Frederikus Hudi, Yusuke Sakai +2
Language exhibits inherent structures, a property that explains both language acquisition and language change. Given this characteristic, we expect language models to manifest thei…
Grammatical Error Correction Evaluation by Optimally Transporting Edit Representation
Takumi Goto, Yusuke Sakai, Taro Watanabe
Automatic evaluation in grammatical error correction (GEC) is crucial for selecting the best-performing systems. Currently, reference-based metrics are a popular choice, which basi…
HalluCitation Matters: Revealing the Impact of Hallucinated References with 300 Hallucinated Papers in ACL Conferences
Yusuke Sakai, Hidetaka Kamigaito, Taro Watanabe
Recently, we have often observed hallucinated citations or references that do not correspond to any existing work in papers under review, preprints, or published papers. Such hallu…
Agreement-Constrained Probabilistic Minimum Bayes Risk Decoding
Koki Natsumi, Hiroyuki Deguchi, Yusuke Sakai +2
Minimum Bayes risk (MBR) decoding generates high-quality translations by maximizing the expected utility of output candidates, but it evaluates all pairwise scores over the candida…
Toward Automatic Safe Driving Instruction: A Large-Scale Vision Language Model Approach
Haruki Sakajo, Hiroshi Takato, Hiroshi Tsutsui +3
Large-scale Vision Language Models (LVLMs) exhibit advanced capabilities in tasks that require visual information, including object detection. These capabilities have promising app…
J-ORA: A Framework and Multimodal Dataset for Japanese Object Identification, Reference, Action Prediction in Robot Perception
Jesse Atuhurra, Hidetaka Kamigaito, Taro Watanabe +1
We introduce J-ORA, a novel multimodal dataset that bridges the gap in robot perception by providing detailed object attribute annotations within Japanese human-robot dialogue scen…
Reliability Crisis of Reference-free Metrics for Grammatical Error Correction
Takumi Goto, Yusuke Sakai, Taro Watanabe
Reference-free evaluation metrics for grammatical error correction (GEC) have achieved high correlation with human judgments. However, these metrics are not designed to evaluate ad…
Decoding Uncertainty: The Impact of Decoding Strategies for Uncertainty Estimation in Large Language Models
Wataru Hashimoto, Hidetaka Kamigaito, Taro Watanabe
Decoding strategies manipulate the probability distribution underlying the output of a language model and can therefore affect both generation quality and its uncertainty. In this…
SinhalaMMLU: A Comprehensive Benchmark for Evaluating Multitask Language Understanding in Sinhala
Ashmari Pramodya, Nirasha Nelki, Heshan Shalinda +6
Large Language Models (LLMs) demonstrate impressive general knowledge and reasoning abilities, yet their evaluation has predominantly focused on global or anglocentric subjects, of…
Revisiting Compositional Generalization Capability of Large Language Models Considering Instruction Following Ability
Yusuke Sakai, Hidetaka Kamigaito, Taro Watanabe
In generative commonsense reasoning tasks such as CommonGen, generative large language models (LLMs) compose sentences that include all given concepts. However, when focusing on in…
Diversity of Transformer Layers: One Aspect of Parameter Scaling Laws
Hidetaka Kamigaito, Ying Zhang, Jingun Kwon +3
Transformers deliver outstanding performance across a wide range of tasks and are now a dominant backbone architecture for large language models (LLMs). Their task-solving performa…