2 citations · 4 across the 5 of their papers we have counts for
6 papers
JUBAKU: An Adversarial Benchmark for Exposing Culturally Grounded Stereotypes in Japanese LLMs
Taihei Shiotani, Masahiro Kaneko, Ayana Niwa +4
Social biases reflected in language are inherently shaped by cultural norms, which vary significantly across regions and lead to diverse manifestations of stereotypes. Existing eva…
JailNewsBench: Multi-Lingual and Regional Benchmark for Fake News Generation under Jailbreak Attacks
Masahiro Kaneko, Ayana Niwa, Timothy Baldwin
Fake news undermines societal trust and decision-making across politics, economics, health, and international relations, and in extreme cases threatens human lives and societal saf…
Rectifying Belief Space via Unlearning to Harness LLMs' Reasoning
Ayana Niwa, Masahiro Kaneko, Kentaro Inui
Large language models (LLMs) can exhibit advanced reasoning yet still generate incorrect answers. We hypothesize that such errors frequently stem from spurious beliefs, proposition…
ExaGPT: Example-Based Machine-Generated Text Detection for Human Interpretability
Ryuto Koike, Masahiro Kaneko, Ayana Niwa +2
Detecting texts generated by Large Language Models (LLMs) could cause grave mistakes due to incorrect decisions, such as undermining students' academic dignity. LLM text detection…
AmbigNLG: Addressing Task Ambiguity in Instruction for NLG
Ayana Niwa, Hayate Iso
We introduce AmbigNLG, a novel task designed to tackle the challenge of task ambiguity in instructions for Natural Language Generation (NLG). Ambiguous instructions often impede th…
Interpretability for Language Learners Using Example-Based Grammatical Error Correction
Masahiro Kaneko, Sho Takase, Ayana Niwa +1
Grammatical Error Correction (GEC) should not focus only on high accuracy of corrections but also on interpretability for language learning. However, existing neural-based GEC mode…