7 citations · 17 across the 9 of their papers we have counts for
9 papers
Contrasting Cognitive Styles in Vision-Language Models: Holistic Attention in Japanese Versus Analytical Focus in English
Ahmed Sabir, Azinovič Gasper, Mengsay Loem +1
Cross-cultural research in perception and cognition has shown that individuals from different cultural backgrounds process visual information in distinct ways. East Asians, for exa…
Joint Extraction Matters: Prompt-Based Visual Question Answering for Multi-Field Document Information Extraction
Mengsay Loem, Taiju Hosaka
Visual question answering (VQA) has emerged as a flexible approach for extracting specific pieces of information from document images. However, existing work typically queries each…
Continual Pre-Training for Cross-Lingual LLM Adaptation: Enhancing Japanese Language Capabilities
Kazuki Fujii, Taishi Nakamura, Mengsay Loem +7
Cross-lingual continual pre-training of large language models (LLMs) initially trained on English corpus allows us to leverage the vast amount of English language resources and red…
Building a Large Japanese Web Corpus for Large Language Models
Naoaki Okazaki, Kakeru Hattori, Hirai Shota +7
Open Japanese large language models (LLMs) have been trained on the Japanese portions of corpora such as CC-100, mC4, and OSCAR. However, these corpora were not created for the qua…
Likelihood-based Mitigation of Evaluation Bias in Large Language Models
Masanari Oi, Masahiro Kaneko, Ryuto Koike +2
Large Language Models (LLMs) are widely used to evaluate natural language generation tasks as automated metrics. However, the likelihood, a measure of LLM's plausibility for a sent…
SAIE Framework: Support Alone Isn't Enough -- Advancing LLM Training with Adversarial Remarks
Mengsay Loem, Masahiro Kaneko, Naoaki Okazaki
Large Language Models (LLMs) can justify or critique their predictions through discussions with other models or humans, thereby enriching their intrinsic understanding of instances…
Exploring Effectiveness of GPT-3 in Grammatical Error Correction: A Study on Performance and Controllability in Prompt-Based Methods
Mengsay Loem, Masahiro Kaneko, Sho Takase +1
Large-scale pre-trained language models such as GPT-3 have shown remarkable performance across various natural language processing tasks. However, applying prompt-based methods wit…
Are Neighbors Enough? Multi-Head Neural n-gram can be Alternative to Self-attention
Mengsay Loem, Sho Takase, Masahiro Kaneko +1
Impressive performance of Transformer has been attributed to self-attention, where dependencies between entire input in a sequence are considered at every position. In this work, w…
ExtraPhrase: Efficient Data Augmentation for Abstractive Summarization
Mengsay Loem, Sho Takase, Masahiro Kaneko +1
Neural models trained with large amount of parallel data have achieved impressive performance in abstractive summarization tasks. However, large-scale parallel corpora are expensiv…