17 citations · 53 across the 35 of their papers we have counts for
37 papers · 1 filter
Once a Tree, Always a Tree? Cross-lingual Transfer of Hierarchical Generalization in Language Models
Shin Yamaguchi, Masaya Taniguchi, Tatsuki Kuribayashi +2
Language models (LMs) often exhibit successful cross-lingual transfer through continued (pre)training, still their underlying process remains unclear. One possibility is that LMs c…
Typological Alignment of Stack-Based Language Models on Mildly Context-Sensitive Artificial Languages
Nadine El-Naggar, Tatsuki Kuribayashi, Ted Briscoe
Some properties of languages, e.g., subject-object-verb (SOV) word order, are more prevalent than others among the thousands of attested natural languages (NLs). Such typological c…
Using LMs to Model the Effects of Context and Coreference during Sentence Comprehension
Kohei Kajikawa, Lin Ai, Tatsuki Kuribayashi +1
Language models (LMs) are often used as a tool to model human language processing. Recent studies suggest that severely restricting LMs' context window improves their fit to human…
Multilingual Idioms in Sentences and Conversations Across High-, Medium-, and Low-Resource Languages
Saeed Almheiri, Bilal Elbouardi, Salsabila Zahirah Pranida +16
Idiomatic expressions pose a major challenge for multilingual NLP because their meanings shift between figurative and literal usage, often requiring context for accurate interpreta…
What Kind of Language is Easy to Language-Model Under Curriculum Learning?
Nadine El-Naggar, Tatsuki Kuribayashi, Ted Briscoe
Many of the thousands of attested languages share common configurations of features, creating a spectrum from typologically very rare (e.g., object-verb-subject word order) or impo…
Dual Alignment Between Language Model Layers and Human Sentence Processing
Tatsuki Kuribayashi, Alex Warstadt, Yohei Oseki +1
A recent study (Kuribayashi et al., 2025) has shown that human sentence processing behavior, typically measured on syntactically unchallenging constructions, can be effectively mod…
An Existence Proof for Neural Language Models That Can Explain Garden-Path Effects via Surprisal
Ryo Yoshida, Shinnosuke Isono, Taiga Someya +2
Surprisal theory hypothesizes that the difficulty of human sentence processing increases linearly with surprisal, the negative log-probability of a word given its context. Computat…
Which Word Orders Facilitate Length Generalization in LMs? An Investigation with GCG-Based Artificial Languages
Nadine El-Naggar, Tatsuki Kuribayashi, Ted Briscoe
Whether language models (LMs) have inductive biases that favor typologically frequent grammatical properties over rare, implausible ones has been investigated, typically using arti…
Syntactic Learnability of Echo State Neural Language Models at Scale
Ryo Ueda, Tatsuki Kuribayashi, Shunsuke Kando +1
What is a neural model with minimum architectural complexity that exhibits reasonable language learning capability? To explore such a simple but sufficient neural language model, w…
Can LLMs Simulate L2-English Dialogue? An Information-Theoretic Analysis of L1-Dependent Biases
Rena Gao, Xuetong Wu, Tatsuki Kuribayashi +6
This study evaluates Large Language Models' (LLMs) ability to simulate non-native-like English use observed in human second language (L2) learners interfered with by their native f…
Can Language Models Learn Typologically Implausible Languages?
Tianyang Xu, Tatsuki Kuribayashi, Yohei Oseki +2
Grammatical features across human languages show intriguing correlations often attributed to learning biases in humans. However, empirical evidence has been limited to experiments…
On Representational Dissociation of Language and Arithmetic in Large Language Models
Riku Kisako, Tatsuki Kuribayashi, Ryohei Sasano
The association between language and (non-linguistic) thinking ability in humans has long been debated, and recently, neuroscientific evidence of brain activity patterns has been c…
Large Language Models Are Human-Like Internally
Tatsuki Kuribayashi, Yohei Oseki, Souhaib Ben Taieb +2
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; Kuribay…
Libra-Leaderboard: Towards Responsible AI through a Balanced Leaderboard of Safety and Capability
Haonan Li, Xudong Han, Zenan Zhai +32
To address this gap, we introduce Libra-Leaderboard, a comprehensive framework designed to rank LLMs through a balanced evaluation of performance and safety. Combining a dynamic le…
Can Input Attributions Explain Inductive Reasoning in In-Context Learning?
Mengyu Ye, Tatsuki Kuribayashi, Goro Kobayashi +1
Interpreting the internal process of neural models has long been a challenge. This challenge remains relevant in the era of large language models (LLMs) and in-context learning (IC…
LLMs Faithfully and Iteratively Compute Answers During CoT: A Systematic Analysis With Multi-step Arithmetics
Keito Kudo, Yoichi Aoki, Tatsuki Kuribayashi +5
This study investigates the internal information flow of large language models (LLMs) while performing chain-of-thought (CoT) style reasoning. Specifically, with a particular inter…
First Heuristic Then Rational: Dynamic Use of Heuristics in Language Model Reasoning
Yoichi Aoki, Keito Kudo, Tatsuki Kuribayashi +4
Multi-step reasoning instruction, such as chain-of-thought prompting, is widely adopted to explore better language models (LMs) performance. We report on the systematic strategy th…
To Drop or Not to Drop? Predicting Argument Ellipsis Judgments: A Case Study in Japanese
Yukiko Ishizuki, Tatsuki Kuribayashi, Yuichiroh Matsubayashi +2
Speakers sometimes omit certain arguments of a predicate in a sentence; such omission is especially frequent in pro-drop languages. This study addresses a question about ellipsis -…
Emergent Word Order Universals from Cognitively-Motivated Language Models
Tatsuki Kuribayashi, Ryo Ueda, Ryo Yoshida +3
The world's languages exhibit certain so-called typological or implicational universals; for example, Subject-Object-Verb (SOV) languages typically use postpositions. Explaining th…
Psychometric Predictive Power of Large Language Models
Tatsuki Kuribayashi, Yohei Oseki, Timothy Baldwin
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 no…
Assessing Step-by-Step Reasoning against Lexical Negation: A Case Study on Syllogism
Mengyu Ye, Tatsuki Kuribayashi, Jun Suzuki +2
Large language models (LLMs) take advantage of step-by-step reasoning instructions, e.g., chain-of-thought (CoT) prompting. Building on this, their ability to perform CoT-style rea…
Second Language Acquisition of Neural Language Models
Miyu Oba, Tatsuki Kuribayashi, Hiroki Ouchi +1
With the success of neural language models (LMs), their language acquisition has gained much attention. This work sheds light on the second language (L2) acquisition of LMs, while…
Transformer Language Models Handle Word Frequency in Prediction Head
Goro Kobayashi, Tatsuki Kuribayashi, Sho Yokoi +1
Prediction head is a crucial component of Transformer language models. Despite its direct impact on prediction, this component has often been overlooked in analyzing Transformers.…
Do Deep Neural Networks Capture Compositionality in Arithmetic Reasoning?
Keito Kudo, Yoichi Aoki, Tatsuki Kuribayashi +4
Compositionality is a pivotal property of symbolic reasoning. However, how well recent neural models capture compositionality remains underexplored in the symbolic reasoning tasks.…