Tohoku University · 컴퓨터과학
Tatsuki Kuribayashi 교수의 연구실은 자연어 처리와 인지 모델링의 융합을 핵심으로 하며, 언어 모델이 인간의 독해 과정을 얼마나 정확히 시뮬레이션할 수 있는지에 초점을 맞춥니다. 특히 언어 모델의 문맥 접근 방식과 인간의 인지적 처리 능력 간 격차를 분석하고, 이를 보완하기 위해 문맥 제약이나 문법적 편향을 통합한 모델 설계를 연구합니다. 또한 논증 구조 분석을 통해 문장 간 논리적 관계를 정량적으로 분석하는 데에도 기여하고 있습니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
Tatsuki Kuribayashi, Yohei Oseki, Takumi Ito, Ryo Yoshida, Masayuki Asahara, Kentaro Inui. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.
Tatsuki Kuribayashi, Hiroki Ouchi, Naoya Inoue, Paul Reisert, Toshinori Miyoshi, Jun Suzuki, Kentaro Inui. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019.
Language models (LMs) have been used in cognitive modeling as well as engineering studies—they compute information-theoretic complexity metrics that simulate humans' cognitive load during reading.This study highlights a limitation of modern neural LMs as the model of choice for this purpose: there is a discrepancy between their context access capacities and that of humans.Our results showed that constraining the LMs' context access improved their simulation of human reading behavior.We also show
Instruction tuning aligns the response of large language models (LLMs) with human preferences.Despite such efforts in human-LLM alignment, we find that instruction tuning does not always make LLMs human-like from a cognitive modeling perspective.More specifically, next-word probabilities estimated by instruction-tuned LLMs are often worse at simulating human reading behavior than those estimated by base LLMs.In addition, we explore prompting methodologies for simulating human reading behavior wi
Language models (LMs) have been used in cognitive modeling as well as engineering studies -- they compute information-theoretic complexity metrics that simulate humans' cognitive load during reading. This study highlights a limitation of modern neural LMs as the model of choice for this purpose: there is a discrepancy between their context access capacities and that of humans. Our results showed that constraining the LMs' context access improved their simulation of human reading behavior. We als
論述構造解析は，小論文などの論述文を解析対象とし，節や文といった談話単位の役割（主張，根拠など），談話単位間の関係（支持，反論など）を予測するタスクである．論述文の自動評価や意見の集約などへの応用可能性から，本タスクは注目を集めている．論述構造解析では，談話単位（スパン）が処理の基本単位となるため，スパンに対する特徴ベクトル表現（分散表現）を，どのように計算するかはモデル設計における重要な点である．本研究では，自然言語処理の諸タスクにおけるスパン分散表現の研究を踏まえ，論述構造解析において効果的な談話単位のスパン分散表現の獲得方法を提案する．文章中の機能的な表現（接続表現）と論理的な筋を構成する内容（命題）というそれぞれの粒度で文章の大域的な文脈情報を捉えることが重要であるという仮定に基づき，提案法では各談話単位に複数のレベルの大域的な文脈情報を取り込む．本スパン分散表現を用いることにより，特にこれまでの手法では同定が困難であった複雑な構造をもつ論述文において，解析性能が向上し，複数のベンチマークデータセット上で最高性能を更新した．また，BERT などの強力な言語モデルから得られる単語
Recent cognitive modeling studies have reported that larger language models (LMs) exhibit a poorer fit to human reading behavior (Oh and Schuler, 2023b; Shain et al., 2024; Kuribayashi et al., 2024), leading to claims of their cognitive implausibility. In this paper, we revisit this argument through the lens of mechanistic interpretability and argue that prior conclusions were skewed by an exclusive focus on the final layers of LMs. Our analysis reveals that next-word probabilities derived from
Instruction tuning aligns the response of large language models (LLMs) with human preferences. Despite such efforts in human--LLM alignment, we find that instruction tuning does not always make LLMs human-like from a cognitive modeling perspective. More specifically, next-word probabilities estimated by instruction-tuned LLMs are often worse at simulating human reading behavior than those estimated by base LLMs. In addition, we explore prompting methodologies for simulating human reading behavio
We examine a methodology using neural language models (LMs) for analyzing the word order of language. This LM-based method has the potential to overcome the difficulties existing methods face, such as the propagation of preprocessor errors in count-based methods. In this study, we explore whether the LMbased method is valid for analyzing the word order. As a case study, this study focuses on Japanese due to its complex and flexible word order. To validate the LM-based method, we test (i) paralle
Abstract Interpretable rationales for model predictions are crucial in practical applications. We develop neural models that possess an interpretable inference process for dependency parsing. Our models adopt instance-based inference, where dependency edges are extracted and labeled by comparing them to edges in a training set. The training edges are explicitly used for the predictions; thus, it is easy to grasp the contribution of each edge to the predictions. Our experiments show that our inst
Tatsuki Kuribayashi, Ryo Ueda, Ryo Yoshida, Yohei Oseki, Ted Briscoe, Timothy Baldwin. Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2024.
Neural language models (LMs) are arguably less data-efficient than humans from a language acquisition perspective. One fundamental question is why this human-LM gap arises. This study explores the advantage of grounded language acquisition, specifically the impact of visual information -- which humans can usually rely on but LMs largely do not have access to during language acquisition -- on syntactic generalization in LMs. Our experiments, following the poverty of stimulus paradigm under two sc
In computational psycholinguistics, various language models have been evaluated against human reading behavior (e.g., eye movement) to build human-like computational models. However, most previous efforts have focused almost exclusively on English, despite the recent trend towards linguistic universal within the general community. In order to fill the gap, this paper investigates whether the established results in computational psycholinguistics can be generalized across languages. Specifically,
We examine a methodology using neural language models (LMs) for analyzing the word order of language. This LM-based method has the potential to overcome the difficulties existing methods face, such as the propagation of preprocessor errors in count-based methods. In this study, we explore whether the LM-based method is valid for analyzing the word order. As a case study, this study focuses on Japanese due to its complex and flexible word order. To validate the LM-based method, we test (i) parall
