Papers by Ryoma Kumon
Fine-Grained Analysis of Shared Syntactic Mechanisms in Language Models (2026.acl-long)
Copied to clipboard
| Challenge: | Recent advances in language models have demonstrated their ability to process linguistic expressions with complex syntactic structures. |
| Approach: | They investigate whether language models employ shared neural mechanisms across different constructions by applying causal interpretability methods at a granular level. |
| Outcome: | The proposed model performance improves on acceptability judgment benchmarks. |
Analysis of the Neglect-Zero Effect in Large Language Models (2026.acl-srw)
Copied to clipboard
| Challenge: | a neglect-zero effect is a human cognitive bias in language processing . it is unclear whether LLMs also exhibit this effect . |
| Approach: | They focus on a human cognitive bias called the *neglect-zero effect* . they propose a paradigm where exposure to a preceding sentence facilitates processing of a subsequent sentence due to their similarity. |
| Outcome: | The proposed paradigm based on priming facilitates processing of a subsequent sentence due to their similarity to the target. |
Bias Mitigation or Cultural Commonsense? Evaluating LLMs with a Japanese Dataset (2025.emnlp-main)
Copied to clipboard
| Challenge: | Recent studies have demonstrated that large language models exhibit social biases . however, debiasing methods may degrade the capabilities of LLMs if they are not properly evaluated . |
| Approach: | They propose a Japanese benchmark to evaluate social biases and cultural commonsense in large language models in a unified format. |
| Outcome: | The proposed method degrades the performance of the LLMs on the cultural commonsense task by 75%. |
Evaluating Structural Generalization in Neural Machine Translation (2024.findings-acl)
Copied to clipboard
| Challenge: | Existing studies have focused on compositional generalization with semantic parsing, but it remains unclear to what extent models can translate sentences that require structural generalization. |
| Approach: | They construct a machine translation dataset that measures compositional generalization with control of words and sentence structures. |
| Outcome: | The proposed model struggle more in structural generalization than in compositional generalization. |
Analyzing the Inner Workings of Transformers in Compositional Generalization (2025.naacl-long)
Copied to clipboard
| Challenge: | Existing studies on compositional generalization abilities of neural models have focused on benchmarks, but the results do not reflect the underlying competence of the model. |
| Approach: | They propose to find an existing subnetwork that contributes to the generalization performance and perform causal analyses on how the model utilizes syntactic features. |
| Outcome: | The proposed model relies on syntactic features but the subnetwork with better generalization performance relies mainly on a non-compositional algorithm . |