Papers by Tatsuki Kuribayashi
Context Limitations Make Neural Language Models More Human-Like (2022.emnlp-main)
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| Challenge: | Language models (LMs) have been used in cognitive modeling and engineering studies to simulate human cognitive load during reading. |
| Approach: | They propose to constrain LMs' context access to improve their simulation of human reading behavior by incorporating syntactic biases into their context access. |
| Outcome: | The proposed model improves the simulation of human reading behavior by incorporating syntactic biases into their context access. |
Topicalization in Language Models: A Case Study on Japanese (2022.coling-1)
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| Challenge: | a recent study has shown that neural language models can capture discourse-level preferences in text generation . a particular aspect of discourse is the topic-comment structure . |
| Approach: | They analyze whether neural language models can capture discourse-level preferences in text generation . they use Japanese language and crowdsourced human topicalization judgment data . |
| Outcome: | The proposed model can capture human-like generalizations in discourse-level linguistic aspects. |
Does Vision Accelerate Hierarchical Generalization in Neural Language Learners? (2025.coling-main)
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| Challenge: | Neural language models (LMs) are arguably less data-efficient than humans from a language acquisition perspective. |
| Approach: | They investigate the advantage of grounded language acquisition over visual input to improve syntactic generalization. |
| Outcome: | The proposed model is less efficient than humans in language acquisition . it shows that visual input helps syntactic generalization, but not vision . |
On the Effect of Hyperparameters in Language Modeling for Computational Linguistics (2026.acl-long)
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| Challenge: | Training language models and examining their linguistic behaviors is a common protocol in computational linguistics for studying linguistic phenomena and modeling human language processing. |
| Approach: | They replicate three prior studies with hyperparameters varied within a practical range and show that modest hyperparametric changes can alter qualitative conclusions about models’ linguistic abilities. |
| Outcome: | The results show that hyperparameter changes can alter qualitative conclusions and reverse the ranking of models. |
LLMs Faithfully and Iteratively Compute Answers During CoT: A Systematic Analysis With Multi-step Arithmetics (2026.findings-eacl)
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Keito Kudo, Yoichi Aoki, Tatsuki Kuribayashi, Shusaku Sone, Masaya Taniguchi, Ana Brassard, Keisuke Sakaguchi, Kentaro Inui
| Challenge: | Specifically, we examine when the LLMs’ answer is (pre)determined, especially before the CoT begins or after, and how strongly the information from CoT specifically has a causal effect on the final answer. |
| Approach: | They examine when the LLMs’ answer is (pre)determined, especially before the CoT begins or after, and how strongly the information from CoT specifically has a causal effect on the final answer. |
| Outcome: | The proposed model can generate reasoning chains while generating the reasoning chain on the fly. |
Modeling Event Salience in Narratives via Barthes’ Cardinal Functions (2020.coling-main)
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| Challenge: | Existing methods for estimating event salience without annotations are prohibitively costly because they require annotators to understand the concept of event salientity. |
| Approach: | They propose to use Barthes’ definition of event salience to compute event salientity without annotations by using a pre-trained language model. |
| Outcome: | The proposed methods outperform baseline methods on folktales with event salience annotation and fine-tuned language model is key factor in improving the methods. |
Langsmith: An Interactive Academic Text Revision System (2020.emnlp-demos)
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| Challenge: | Currently, diversity and inclusion initiatives in the academic community are encouraged . however, writing papers in English can be a daunting task . |
| Approach: | They propose a system that helps non-native English speakers to write papers in English . the system can suggest fluent, academic-style sentences based on their rough, incomplete phrases or sentences . |
| Outcome: | The proposed system can help non-native English speakers write papers in English . the system can suggest fluent, academic-style sentences based on their rough sentences . |
An Existence Proof for Neural Language Models That Can Explain Garden-Path Effects via Surprisal (2026.acl-long)
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| Challenge: | Surprisal theory claims that difficulty of sentences increases linearly with surprise . a neural LM that can explain garden-path effects cannot be built, says a new study . |
| Approach: | They propose to fine-tune neural LMs to better align surprisal-based reading-time estimates with actual reading times. |
| Outcome: | a new study shows that fine-tuned neural LMs do not overfit on held-out items . the results show that they improve predictive power for human reading times . |
Instance-Based Learning of Span Representations: A Case Study through Named Entity Recognition (2020.acl-main)
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Hiroki Ouchi, Jun Suzuki, Sosuke Kobayashi, Sho Yokoi, Tatsuki Kuribayashi, Ryuto Konno, Kentaro Inui
| Challenge: | Recent neural networks can induce good span feature representations and achieve high performance in structured prediction tasks. |
| Approach: | They propose an instance-based learning method that learns similarities between spans . they aim to build models that have high interpretability without sacrificing performance . |
| Outcome: | The proposed method improves interpretability without sacrificing performance. |
Transformer Language Models Handle Word Frequency in Prediction Head (2023.findings-acl)
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| Challenge: | Prediction head is a crucial component of Transformer language models. Despite its direct impact on prediction, its characteristics have been overlooked in previous analyses. |
| Approach: | They examine the inner workings of the prediction head, specifically the bias parameters, and quantify the effect of controlling their frequency biases on text generation. |
| Outcome: | The prediction head is a crucial component of the Transformer language models. |
Empirical Investigation of Neural Symbolic Reasoning Strategies (2023.findings-eacl)
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Yoichi Aoki, Keito Kudo, Tatsuki Kuribayashi, Ana Brassard, Masashi Yoshikawa, Keisuke Sakaguchi, Kentaro Inui
| Challenge: | Neural reasoning accuracy improves when generating intermediate reasoning steps. |
| Approach: | They decompose the reasoning strategy w.r.t. step granularity and chaining strategy. |
| Outcome: | The proposed reasoning strategy significantly affects performance in a symbolic reasoning dataset. |
An Empirical Study of Span Representations in Argumentation Structure Parsing (P19-1)
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Tatsuki Kuribayashi, Hiroki Ouchi, Naoya Inoue, Paul Reisert, Toshinori Miyoshi, Jun Suzuki, Kentaro Inui
| Challenge: | Argumentation structure parsing (ASP) is a task of identifying argumentation structures in argumentative text. |
| Approach: | They propose to exploit neural network-based span representations for ASP to improve performance . they also propose task-dependent extensions for a parser that can be used to parse arguments . |
| Outcome: | The proposed model outperforms neural network-based approaches for argumentation structure parsing (ASP) it also provides some challenging types of instances to be parsed. |
Which Word Orders Facilitate Length Generalization in LMs? An Investigation with GCG-Based Artificial Languages (2025.emnlp-main)
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| Challenge: | Whether language models have inductive biases favoring typologically frequent grammatical properties over rare, implausible ones has been investigated, typically using artificial languages (ALs). |
| Approach: | They extend their context-free AL formalization by adopting Generalized Categorial Grammar (GCG) . they also examine the generalization ability of LMs to process unseen longer test sentences . |
| Outcome: | The proposed models better capture features of natural languages and can process unseen longer test sentences. |
Can Input Attributions Explain Inductive Reasoning in In-Context Learning? (2025.findings-acl)
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| Challenge: | interpreting the internal process of neural models has long been a challenge . despite rapid progress, there are still questions bridging the IA and MI eras . |
| Approach: | They propose to use input attribution methods to interpret in-context learning . they find that a certain simple IA method works best in large models . |
| Outcome: | The proposed method is the best for interpreting LLM-based ICL, but the larger the model, the harder it is to interpret it. |
Do Deep Neural Networks Capture Compositionality in Arithmetic Reasoning? (2023.eacl-main)
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Keito Kudo, Yoichi Aoki, Tatsuki Kuribayashi, Ana Brassard, Masashi Yoshikawa, Keisuke Sakaguchi, Kentaro Inui
| Challenge: | Using a pre-trained dataset, we examine how well recent neural models capture compositionality in symbolic reasoning tasks. |
| Approach: | They propose a skill tree on compositionality that defines hierarchical levels of complexity along with three compositionality dimensions: systematicity, productivity, and substitutivity. |
| Outcome: | The proposed model struggled most with systematicity, performing poorly even with relatively simple compositions. |
To Drop or Not to Drop? Predicting Argument Ellipsis Judgments: A Case Study in Japanese (2024.lrec-main)
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| Challenge: | Speakers sometimes omit certain arguments of a predicate in a sentence; such omission is especially frequent in pro-drop languages. |
| Approach: | They collect large-scale human annotations of whether and why a particular argument should be omitted across over 2,000 data points in Japanese, a prototypical pro-drop language. |
| Outcome: | The proposed model can explain why certain arguments are omitted in Japanese, a prototypical pro-drop language. |
Incorporating Residual and Normalization Layers into Analysis of Masked Language Models (2021.emnlp-main)
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| Challenge: | Transformer architecture is composed of multi-head attention, which has been extensively analyzed. |
| Approach: | They extended the scope of the analysis of Transformers from solely the attention patterns to the whole attention block, i.e., multi-head attention, residual connection, and layer normalization. |
| Outcome: | The proposed method incorporates the whole attention block, i.e., multi-head attention, residual connection, and layer normalization into the analysis. |
Can Language Models Learn Typologically Implausible Languages? (2026.tacl-1)
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| Challenge: | Language models provide a naturalistic framework for studying artificial language learning . authors: typological universals and tendencies are thought to be caused by a learning bias . |
| Approach: | They propose to train LMs on highly naturalistic counterfactual versions of English and Japanese . they show that LM learn subtly implausible languages more slowly . |
| Outcome: | The proposed language models learn subtly implausible languages more slowly compared to human models . the findings suggest that LMs exhibit typologically aligned learning preferences . |
Can LLMs Simulate L2-English Dialogue? An Information-Theoretic Analysis of L1-Dependent Biases (2025.acl-long)
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Rena Wei Gao, Xuetong Wu, Tatsuki Kuribayashi, Mingrui Ye, Siya Qi, Carsten Roever, Yuanxing Liu, Zheng Yuan, Jey Han Lau
| Challenge: | Large Language Models (LLMs) can simulate non-native-like English use observed in human second language (L2) learners interfered with by their native first language (N1) knowledge. |
| Approach: | They use large language models to simulate non-native-like English use observed in human second language (L2) learners, and then compare their outputs to real L2 learner data. |
| Outcome: | The proposed models replicate L1-dependent patterns observed in human second language (L2) learners, with distinct influences from various languages. |
Attention is Not Only a Weight: Analyzing Transformers with Vector Norms (2020.emnlp-main)
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| Challenge: | Attention is a key component of Transformers, which have achieved considerable success in natural language processing. |
| Approach: | They propose to integrate attention weights and the norm of transformed input vectors into a norm-based analysis that incorporates the norm. |
| Outcome: | The proposed analysis shows that attention weights alone determine the output of attention and that reasonable word alignment can be extracted from attention mechanisms of Transformers. |
Language Models as an Alternative Evaluator of Word Order Hypotheses: A Case Study in Japanese (2020.acl-main)
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| Challenge: | a method using neural language models (LMs) for analyzing the word order of language is currently lacking. |
| Approach: | They propose a method using neural language models to analyze the word order in Japanese . they test whether there is a parallel between LMs and human word order preference . |
| Outcome: | The proposed method is validated by comparing it with other linguistic studies. |
Multilingual Idioms in Sentences and Conversations Across High-, Medium-, and Low-Resource Languages (2026.acl-long)
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Saeed Almheiri, Bilal Elbouardi, Salsabila Zahirah Pranida, Irina Nikishina, Ashwath Rao B, Parameswari Krishnamurthy, Muhammad Cendekia Airlangga, Rifo Ahmad Genadi, Nguyen Phan Gia Bao, Amir Hossein Yari, Hawau Olamide Toyin, Nurdaulet Mukhituly, Mena Attia, Besher Hassan, Ahmad Fathan Hidayatullah, Tatsuki Kuribayashi, Haonan Li, Suma Bhat, Fajri Koto
| Challenge: | idioms are a major challenge for multilingual NLP because their meanings shift between figurative and literal usage, often requiring context for accurate interpretation. |
| Approach: | They propose a multilingual idiom dataset that provides idiomatic expressions in both sentence-level and conversational contexts. |
| Outcome: | The proposed model performs well with low-resource idioms, but lacks contextual inference. |
Second Language Acquisition of Neural Language Models (2023.findings-acl)
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| Challenge: | a recent study examined the cross-lingual transferability of neural language models . previous studies focused on their first language acquisition . |
| Approach: | They propose to pretrain bilingual LMs with a scenario similar to human L2 acquisition . they find that pretraining accelerated their linguistic generalization in L2 . |
| Outcome: | The results show that pretraining bilingual LMs accelerates their linguistic generalizations . the results clarify their (non-)human-like L2 acquisition in particular aspects . |
Dual Alignment Between Language Model Layers and Human Sentence Processing (2026.acl-long)
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| Challenge: | Existing studies have demonstrated both the successes and limitations of accurate predictability estimation by modern LMs in cognitive modeling. |
| Approach: | They propose to use internal layers to better estimate human cognitive effort observed in syntactic ambiguity processing in English. |
| Outcome: | The proposed models can be modeled using surprisal from early layers of large language models (LLMs) this raises the question whether such advantages extend to more syntactically challenging constructions, where surprised estimates underestimate human cognitive effort. |
Instance-Based Neural Dependency Parsing (2021.tacl-1)
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Hiroki Ouchi, Jun Suzuki, Sosuke Kobayashi, Sho Yokoi, Tatsuki Kuribayashi, Masashi Yoshikawa, Kentaro Inui
| Challenge: | Existing models that use instance-based inference for dependency parsing are difficult to understand for humans. |
| Approach: | They develop neural models that adopt an interpretable inference process for dependency parsing. |
| Outcome: | The proposed models achieve competitive accuracy with standard neural models and have plausibility of instance-based explanations. |
Lower Perplexity is Not Always Human-Like (2021.acl-long)
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| Challenge: | Existing efforts to build human-like computational models have focused on English . a cross-lingual evaluation is needed to build such models, but current research has focused on Japanese . |
| Approach: | They re-examine an established generalization that lower perplexity is not always human-like in Japanese . they propose a cross-lingual evaluation to build human-type computational models . |
| Outcome: | The proposed model lacks universality and lower perplexity is not always human-like . the results suggest a cross-lingual evaluation will be necessary to build human-type models . |
TEASPN: Framework and Protocol for Integrated Writing Assistance Environments (D19-3)
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| Challenge: | TEASPN is an open-source protocol for integrated writing assistance environments . authors propose that developers and researchers can integrate the latest developments in natural language processing with low cost. |
| Approach: | They propose a protocol and framework for integrating writing aids with writing software. |
| Outcome: | The proposed protocol standardizes the way writing software communicates with servers that implement such technologies, allowing developers and researchers to integrate the latest developments in natural language processing (NLP) with low cost. |
Assessing Step-by-Step Reasoning against Lexical Negation: A Case Study on Syllogism (2023.emnlp-main)
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| Challenge: | Large language models (LLMs) take advantage of step-by-step reasoning instructions . negation is a core linguistic phenomenon that is difficult to process . |
| Approach: | They examine the step-by-step reasoning ability of large language models with a focus on negation . negation is a core linguistic phenomenon that is difficult to process . |
| Outcome: | The proposed models perform better when using chain-of-thought prompting . the results highlight unique limitations in each LLM family . |
Psychometric Predictive Power of Large Language Models (2024.findings-naacl)
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| Challenge: | despite efforts to align large language models with human preferences, instruction tuning does not always make LLMs human-like from a cognitive modeling perspective. |
| Approach: | They find that instruction tuning does not always make large language models human-like from a cognitive perspective. |
| Outcome: | The proposed prompts improve predictive power but are still inferior to small base models. |
First Heuristic Then Rational: Dynamic Use of Heuristics in Language Model Reasoning (2024.emnlp-main)
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Yoichi Aoki, Keito Kudo, Tatsuki Kuribayashi, Shusaku Sone, Masaya Taniguchi, Keisuke Sakaguchi, Kentaro Inui
| Challenge: | Explicit multi-step reasoning is widely adopted to improve the performance of language models. |
| Approach: | They propose a systematic reasoning strategy that LMs use to solve multi-step reasoning tasks. |
| Outcome: | The proposed strategy improves the performance of language models by combining heuristics with rational strategies. |