Annotating Errors in English Learners’ Written Language Production: Advancing Automated Written Feedback Systems, Steven Coyne, Diana Galvan-Sosa, Ryan Spring, Camélia Guerraoui, Michael Zock, Keisuke Sakaguchi, and Kentaro Inui, AIED 2025.
Repetition Neurons: How Do Language Models Produce Repetitions?, Tatsuya Hiraoka, Kentaro Inui, NAACL 2025. 📄 Paper
LLMs sometimes repeat, repeat, repeat, repeat themselves. To uncover the internal mechanism, this paper introduces "repetition neurons", regarded as skill neurons responsible for the repetition problem in text generation tasks. These neurons become progressively more active as the repetition continues.
The Geometry of Numerical Reasoning: Language Models Compare Numeric Properties in Linear Subspaces, Ahmed Oumar El-Shangiti, Tatsuya Hiraoka, Hilal AlQuabeh, Benjamin Heinzerling, Kentaro Inui, NAACL 2025. 📄 Paper
Recent work has analyzed how knowledge is represented in activation space. Building on this line of inquiry, this paper examines how LLMs leverage the linear subspace of entity-numerical attributes when answering questions involving numeric comparisons—for example, "Was Cristiano born before Messi?"
Weight-based Analysis of Detokenization in Language Models: Understanding the First Stage of Inference Without Inference, Go Kamoda, Benjamin Heinzerling, Tatsuro Inaba, Keito Kudo, Keisuke Sakaguchi, Kentaro Inui, NAACL 2025 Findings. 📄 Paper
In LLMs, early layers transform subword tokens into more meaningful representations that form the model's inner vocabulary. This paper demonstrates that several important aspects of this detokenization stage can be understood purely by analyzing model weights.
MQM-Chat: Multidimensional Quality Metrics for Chat Translation, Yunmeng Li, Jun Suzuki, Makoto Morishita, Kaori Abe and Kentaro Inui, COLING 2025. 📄 Paper
This paper introduces MQM-Chat, a new multidimensional quality metric designed specifically for chat translations. It encompasses seven error types, including three that are unique to chat translations, allowing for the evaluation of both lexical and semantic accuracy in chat translation tasks.
SubRegWeigh: Effective and Efficient Annotation Weighing with Subword Regularization, Kohei Tsuji, Tatsuya Hiraoka, Yuchang Cheng, Tomoya Iwakura, COLING 2025. 📄 Paper
NLP datasets may still contain annotation errors, even when they are manually annotated. This paper introduces SubRegWeigh, a time-saving method that leverages subword regularization to simulate multiple error detection models for identifying annotation errors in NLP datasets.
ACORN: Aspect-wise Commonsense Reasoning Explanation Evaluation, Ana Brassard, Benjamin Heinzerling, Keito Kudo, Keisuke Sakaguchi, Kentaro Inui, COLM 2025. 📄 Paper
Assessing the quality of free-text explanations remains challenging for LLMs due to their multifaceted, subjective, and labor-intensive nature. This paper introduces ACORN, a dataset of free-text explanations paired with aspect-wise quality ratings.
Monotonic Representation of Numeric Properties in Language Models, Benjamin Heinzerling, Kentaro Inui, ACL 2024. 📄 Paper
How is factual knowledge involving numeric properties such as "Karl Popper was born in 1902" encoded in the model’s internal representations? This paper introduces a method for finding and editing representations of numeric properties by identifying interpretable, monotonically encoded directions.
A Large Collection of Model-generated Contradictory Responses for Consistency-aware Dialogue Systems, Shiki Sato, Reina Akama, Jun Suzuki, Kentaro Inui, ACL 2024 Findings. 📄 Paper
During interactions with a model, contradictory responses can undermine user trust and disrupt dialogue coherence. This paper tackles that challenge head-on by constructing the first large-scale dataset of model-generated contradictions.
Analyzing Feed-Forward Blocks in Transformers through the Lens of Attention Maps, Goro Kobayashi, Tatsuki Kuribayashi, Sho Yokoi, Kentaro Inui, ICLR 2024. 📄 Paper
Interpreting the internals of Transformer models remains a pivotal challenge. This paper examines the role of feed-forward (FF) blocks in Transformers, which was unexplored, by visualizing their impact on input contextualization through attention maps.
Representational Analysis of Binding in Language Models, Qin Dai, Benjamin Heinzerling, Kentaro Inui, EMNLP 2024. 📄 Paper
This paper delves into the mechanism of in-context entity tracking, exploring how language models seamlessly bind entities to their corresponding attributes from a given context. It newly introduces the Ordering ID captured by entity activations, which directly determines binding behavior.
First Heuristic Then Rational: Dynamic Use of Heuristics in Language Model Reasoning, Yoichi Aoki, Keito Kudo, Tatsuki Kuribayashi, Shusaku Sone, Masaya Taniguchi, Keisuke Sakaguchi, Kentaro Inui, EMNLP 2024. 📄 Paper
This paper reports on the systematic strategy that language models employ in multi-step reasoning processes, revealing that they rely on heuristics such as lexical overlap in the early stages, with this reliance diminishing as they advance toward the final answer.
Flee the Flaw: Annotating the Underlying Logic of Fallacious Arguments Through Templates and Slot-filling, Irfan Robbani, Paul Reisert, Surawat Pothong, Naoya Inoue, Camélia Guerraoui, Wenzhi Wang, Shoichi Naito, Jungmin Choi, Kentaro Inui, EMNLP 2024. 📄 Paper
In education, counterarguments are used to boost critical thinking skills, but giving personalized feedback to students can be challenging for teachers. This paper introduces Counter-Argument Logical Structure Analysis (CALSA), a novel approach that breaks down counterarguments in debates into ten clear logical patterns, offering a promising pathway toward automating effective feedback.
Designing Logic Pattern Templates for Counter-Argument Logical Structure Analysis, Shoichi Naito, Wenzhi Wang, Paul Reisert, Naoya Inoue, Camélia Guerraoui, Kenshi Yamaguchi, Jungmin Choi, Irfan Robbani, Surawat Pothong, Kentaro Inui, EMNLP 2024 Findings. 📄 Paper
The Curse of Popularity: Popular Entities Have Catastrophic Side Effects when Deleting Knowledge from Language Models, Ryosuke Takahashi, Go Kamoda, Benjamin Heinzerling, Keisuke Sakaguchi, Kentaro Inui, NAACL 2024 SRW. 📄 Paper
Language models encode world knowledge in their internal parameters through training. This paper investigates the deletion of such encoded knowledge from the model and analyzes the relationship between deletion side effects and the associated entities using a synthetic knowledge graph.
How Well Do Vision Models Encode Diagram Attributes?, Haruto Yoshida, Keito Kudo, Yoichi Aoki, Ryota Tanaka, Itsumi Saito, Keisuke Sakaguchi, Kentaro Inui, ACL 2024 SRW.
Vision models such as CLIP have been used in research on diagram understanding and generation. This study examines an unexplored capability of these models, specifically, whether they can accurately identify diagram attributes including node colors, shapes, edge colors, and connection patterns.
Teach Me How to Argue: A Survey on NLP Feedback Systems in Argumentation, Camelia Guerraoui, Paul Reisert, Naoya Inoue, Farjana Sultana Mim, Keshav Singh, Jungmin Choi, Irfan Robbani, Shoichi Naito, Wenzhi Wang, Kentaro Inui, 10th Workshop on Argument Mining. 📄 Paper
While current models can assess argument quality, they often fail to provide constructive feedback explaining the basis of their evaluations. This survey explores current NLP feedback systems by categorizing them into four key dimensions—Richness, Visualization, Interactivity, and Personalization.
Contrastive Learning-based Sentence Encoders Implicitly Weight Informative Words, Hiroto Kurita, Goro Kobayashi, Sho Yokoi, Kentaro Inui, EMNLP 2023 Findings. 📄 Paper
The performance of sentence encoders can be significantly improved by fine-tuning with contrastive loss. However, what characteristics do models acquire during contrastive learning? This paper reveals these characteristics by shedding light on the inner workings of word weighting.
Investigating the Effectiveness of Multiple Expert Models Collaboration, Ikumi Ito, Takumi Ito, Jun Suzuki, Kentaro Inui, EMNLP 2023 Findings. 📄 Paper
To create a translation system that excels across diverse domains, this study employs a Multiple Expert Models Collaboration strategy that aggregates the specialized knowledge of individual domain-specific experts and validates the effectiveness.
Test-time Augmentation for Factual Probing, Go Kamoda, Benjamin Heinzerling, Keisuke Sakaguchi, Kentaro Inui, EMNLP 2023 Findings. 📄 Paper
Factual probing, a method that uses prompts to assess a model's world knowledge, faces the challenge that slight variations in the prompt can drastically change the results. To tackle this issue, the study introduces test-time augmentation, which augments and ensembles prompts to reduce sensitivity.
RealTime QA: What’s the Answer Right Now?, Jungo Kasai, Keisuke Sakaguchi, Yoichi Takahashi, Ronan Le Bras, Akari Asai, Xinyan Velocity Yu, Dragomir Radev, Noah A. Smith, Yejin Choi, Kentaro Inui, NeurIPS 2023. 📄 Paper
While answers to questions can evolve over time, traditional QA datasets have been built on static assumptions. This paper presents REALTIME QA, a dynamic platform that announces questions and evaluates systems on a regular basis.
Take No Shortcuts! Stick to the Rubric: A Method for Building Trustworthy Short Answer Scoring Models, Yuya Asazuma, Hiroaki Funayama, Yuichiroh Matsubayashi, Tomoya Mizumoto, Paul Reisert, Kentaro Inui, HELMeTO 2023. 📄 Paper
This paper introduces a novel strategy to enhance the trustworthiness of "Short Answer Scoring" systems in educational settings by aligning response features with rubric criteria to mitigate shortcut learning based on superficial cues in the training data.
Deterministic Compression of Word Embeddings, Yuki Nakamura, Jun Suzuki, Takumi Ito, Kentaro Inui, IEEE Access (2025). 📄 Paper
Reducing the memory required by word embeddings while still maintaining performance is crucial given their large vocabulary and high dimensionality. This paper presents a new compression method that uses a deterministic convex optimization process to produce stable and reproducible representations.
Rectifying Belief Space via Unlearning to Harness LLMs' Reasoning. Ayana Niwa, Masahiro Kaneko, Kentaro Inui. 📄 Paper
Why do LLMs sometimes generate incorrect answers, even as their capabilities continue to improve? This study hypothesizes that the model's flawed reasoning stems from its reliance on incorrect beliefs, and it demonstrates that correcting the belief space leads to improved performance.
RECALL: Library-Like Behavior In Language Models is Enhanced by Self-Referencing Causal Cycles. Munachiso Nwadike, Zangir Iklassov, Toluwani Aremu, Tatsuya Hiraoka, Velibor Bojkovic, Benjamin Heinzerling, Hilal Alqaubeh, Martin Takáč, Kentaro Inui. 📄 Pape
This paper introduces a new method called the self-referencing causal cycle (RECALL). It helps large language models avoid the reversal curse, which is a problem that comes from one-way reasoning. RECALL uses special "cycle tokens" to connect different parts of the training data.
FinchGPT: a Transformer based language model for birdsong analysis. Kosei Kobayashi, Kosuke Matsuzaki, Masaya Taniguchi, Keisuke Sakaguchi, Kentaro Inui, Kentaro Abe. 📄 Paper
This paper asks whether the long-range dependencies that define human language also appear in animal communication. It employs Transformer models to explore this idea in Bengalese finch songs, which are marked by highly variable and complex syllable sequences.
Large Language Models Are Human-Like Internally. Tatsuki Kuribayashi, Yohei Oseki, Souhaib Ben Taieb, Kentaro Inui, Timothy Baldwin. 📄 Paper
This paper challenges recent claims from cognitive modeling studies that LLMs poorly align with human reading behavior, showing that focusing solely on their final layers can be misleading, as revealed by a mechanistic analysis of their internal layers.