Author name cluster

Kyosuke Nishida

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7 papers

2 author rows

ICML Conference 2025 Conference Paper

Portable Reward Tuning: Towards Reusable Fine-Tuning across Different Pretrained Models

Daiki Chijiwa
Taku Hasegawa
Kyosuke Nishida
Kuniko Saito
Susumu Takeuchi

While foundation models have been exploited for various expert tasks with their fine-tuned parameters, any foundation model will be eventually outdated due to its old knowledge or limited capability, and thus should be replaced by a new foundation model. Subsequently, to benefit from its latest knowledge or improved capability, the new foundation model should be fine-tuned on each task again, which incurs not only the additional training cost but also the maintenance cost of the task-specific data. Existing work address this problem by inference-time tuning, i. e. , modifying the output probability from the new foundation model by the outputs from the old foundation model and its fine-tuned model, which involves an additional inference cost by the latter two models. In this paper, we explore a new fine-tuning principle (which we call portable reward tuning; PRT) that reduces the inference cost by its nature, based on the reformulation of fine-tuning as the reward maximization with Kullback-Leibler regularization. Specifically, instead of fine-tuning parameters of the foundation models, PRT trains the reward model explicitly through the same loss as in fine-tuning. During inference, the reward model can be used with any foundation model (with the same set of vocabularies or labels) through the formulation of reward maximization. Experimental results, including both vision and language models, show that the PRT-trained model can achieve comparable accuracy with less inference cost, in comparison to the existing work of inference-time tuning.

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AAAI Conference 2025 Conference Paper

ToMATO: Verbalizing the Mental States of Role-Playing LLMs for Benchmarking Theory of Mind

Kazutoshi Shinoda
Nobukatsu Hojo
Kyosuke Nishida
Saki Mizuno
Keita Suzuki
Ryo Masumura
Hiroaki Sugiyama
Kuniko Saito

Existing Theory of Mind (ToM) benchmarks diverge from real-world scenarios in three aspects: 1) they assess a limited range of mental states such as beliefs, 2) false beliefs are not comprehensively explored, and 3) the diverse personality traits of characters are overlooked. To address these challenges, we introduce ToMATO, a new ToM benchmark formulated as multiple-choice QA over conversations. ToMATO is generated via LLM-LLM conversations featuring information asymmetry. By employing a prompting method that requires role-playing LLMs to verbalize their thoughts before each utterance, we capture both first- and second-order mental states across five categories: belief, intention, desire, emotion, and knowledge. These verbalized thoughts serve as answers to questions designed to assess the mental states of characters within conversations. Furthermore, the information asymmetry introduced by hiding thoughts from others induces the generation of false beliefs about various mental states. Assigning distinct personality traits to LLMs further diversifies both utterances and thoughts. ToMATO consists of 5.4k questions, 753 conversations, and 15 personality trait patterns. Our analysis shows that this dataset construction approach frequently generates false beliefs due to the information asymmetry between role-playing LLMs, and effectively reflects diverse personalities. We evaluate nine LLMs on ToMATO and find that even GPT-4o mini lags behind human performance, especially in understanding false beliefs, and lacks robustness to various personality traits.

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ICLR Conference 2025 Conference Paper

Wavelet-based Positional Representation for Long Context

Yui Oka
Taku Hasegawa
Kyosuke Nishida
Kuniko Saito

In the realm of large-scale language models, a significant challenge arises when extrapolating sequences beyond the maximum allowable length. This is because the model's position embedding mechanisms are limited to positions encountered during training, thus preventing effective representation of positions in longer sequences. We analyzed conventional position encoding methods for long contexts and found the following characteristics. (1) When the representation dimension is regarded as the time axis, Rotary Position Embedding (RoPE) can be interpreted as a restricted wavelet transform using Haar-like wavelets. However, because it uses only a fixed scale parameter, it does not fully exploit the advantages of wavelet transforms, which capture the fine movements of non-stationary signals using multiple scales (window sizes). This limitation could explain why RoPE performs poorly in extrapolation. (2) Previous research as well as our own analysis indicates that Attention with Linear Biases (ALiBi) functions similarly to windowed attention, using windows of varying sizes. However, it has limitations in capturing deep dependencies because it restricts the receptive field of the model. From these insights, we propose a new position representation method that captures multiple scales (i.e., window sizes) by leveraging wavelet transforms without limiting the model's attention field. Experimental results show that this new method improves the performance of the model in both short and long contexts. In particular, our method allows extrapolation of position information without limiting the model's attention field.

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AAAI Conference 2024 Conference Paper

InstructDoc: A Dataset for Zero-Shot Generalization of Visual Document Understanding with Instructions

Ryota Tanaka
Taichi Iki
Kyosuke Nishida
Kuniko Saito
Jun Suzuki

We study the problem of completing various visual document understanding (VDU) tasks, e.g., question answering and information extraction, on real-world documents through human-written instructions. To this end, we propose InstructDoc, the first large-scale collection of 30 publicly available VDU datasets, each with diverse instructions in a unified format, which covers a wide range of 12 tasks and includes open document types/formats. Furthermore, to enhance the generalization performance on VDU tasks, we design a new instruction-based document reading and understanding model, InstructDr, that connects document images, image encoders, and large language models (LLMs) through a trainable bridging module. Experiments demonstrate that InstructDr can effectively adapt to new VDU datasets, tasks, and domains via given instructions and outperforms existing multimodal LLMs and ChatGPT without specific training.

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AAAI Conference 2023 Conference Paper

SlideVQA: A Dataset for Document Visual Question Answering on Multiple Images

Ryota Tanaka
Kyosuke Nishida
Kosuke Nishida
Taku Hasegawa
Itsumi Saito
Kuniko Saito

Visual question answering on document images that contain textual, visual, and layout information, called document VQA, has received much attention recently. Although many datasets have been proposed for developing document VQA systems, most of the existing datasets focus on understanding the content relationships within a single image and not across multiple images. In this study, we propose a new multi-image document VQA dataset, SlideVQA, containing 2.6k+ slide decks composed of 52k+ slide images and 14.5k questions about a slide deck. SlideVQA requires complex reasoning, including single-hop, multi-hop, and numerical reasoning, and also provides annotated arithmetic expressions of numerical answers for enhancing the ability of numerical reasoning. Moreover, we developed a new end-to-end document VQA model that treats evidence selection and question answering as a unified sequence-to-sequence format. Experiments on SlideVQA show that our model outperformed existing state-of-the-art QA models, but that it still has a large gap behind human performance. We believe that our dataset will facilitate research on document VQA.

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AAAI Conference 2021 Conference Paper

VisualMRC: Machine Reading Comprehension on Document Images

Ryota Tanaka
Kyosuke Nishida
Sen Yoshida

Recent studies on machine reading comprehension have focused on text-level understanding but have not yet reached the level of human understanding of the visual layout and content of real-world documents. In this study, we introduce a new visual machine reading comprehension dataset, named VisualMRC, wherein given a question and a document image, a machine reads and comprehends texts in the image to answer the question in natural language. Compared with existing visual question answering (VQA) datasets that contain texts in images, VisualMRC focuses more on developing natural language understanding and generation abilities. It contains 30, 000+ pairs of a question and an abstractive answer for 10, 000+ document images sourced from multiple domains of webpages. We also introduce a new model that extends existing sequence-to-sequence models, pre-trained with largescale text corpora, to take into account the visual layout and content of documents. Experiments with VisualMRC show that this model outperformed the base sequence-to-sequence models and a state-of-the-art VQA model. However, its performance is still below that of humans on most automatic evaluation metrics. The dataset will facilitate research aimed at connecting vision and language understanding.

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AAAI Conference 2017 Conference Paper

Understanding the Semantic Structures of Tables with a Hybrid Deep Neural Network Architecture

Kyosuke Nishida
Kugatsu Sadamitsu
Ryuichiro Higashinaka
Yoshihiro Matsuo

We propose a new deep neural network architecture, TabNet, for table type classiﬁcation. Table type is essential information for exploring the power of Web tables, and it is important to understand the semantic structures of tables in order to classify them correctly. A table is a matrix of texts, analogous to an image, which is a matrix of pixels, and each text consists of a sequence of tokens. Our hybrid architecture mirrors the structure of tables: its recurrent neural network (RNN) encodes a sequence of tokens for each cell to create a 3d table volume like image data, and its convolutional neural network (CNN) captures semantic features, e. g. , the existence of rows describing properties, to classify tables. Experiments using Web tables with various structures and topics demonstrated that TabNet achieved considerable improvements over state-of-the-art methods specialized for table classiﬁcation and other deep neural network architectures.

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