6 citations · 14 across the 20 of their papers we have counts for
23 papers
ReRank: Listwise Reranking with Reasoning-Conditioned Retrieval Heads
Ruiyi Yan, Zhuoyuan Mao, Yugo Murawaki +1
Attention-based rerankers score each candidate by the attention mass that retrieval heads route to it from the question, but the question, serving as a fixed query, carries limited…
APRiL: An Open-ended Benchmark for Chinese Social Awareness in Large Language Models
Tian Lan, Han Zhang, Shanshan Wang +3
Large language models are increasingly used in social interactions, where appropriate responses require recognizing implicit cues, inferring mental states, identifying conflicts, a…
A Comprehensive Survey on Linguistic Steganography: Methods, Countermeasures, Evaluation, and Challenges
Ruiyi Yan, Chenhui Chu, Zhongliang Yang +1
Linguistic steganography hides secret messages in natural language text. Large language models (LLMs) have reshaped the field, but a systematic account of how these scattered advan…
HiTMS: A High-Throughput Multi-Stream Linguistic Steganography Framework
Ruiyi Yan, Zhongliang Yang, Yugo Murawaki
Generative linguistic steganography conceals secret bits within the sampling randomness of large language models. Existing schemes are single-stream, conveying an entire secret thr…
Dango: A Strictly L1-Only Large Language Model for Studying Second Language Acquisition
Shiho Matta, Yin Jou Huang, Fei Cheng +3
We introduce Dango, a 1.8B-parameter large language model designed for controlled studies of L1-to-L2 (Japanese-to-English) transfer in second language acquisition (SLA). While pre…
Scaling LLM Reasoning from Minimal Labels: A Semi-Supervised Framework with a Lightweight Verifier
Keizo Kato, Chenhui Chu, Yugo Murawaki +1
For the development of Large language models (LLMs), recent approaches to generating pseudo intermediate reasoning have shown remarkable progress. But they typically rely on large…
Anchored Sliding Window: Toward Robust and Imperceptible Linguistic Steganography
Ruiyi Yan, Shiao Meng, Yugo Murawaki
Linguistic steganography based on language models typically assumes that steganographic texts are transmitted without alteration, making them fragile to even minor modifications. W…
Efficient Provably Secure Linguistic Steganography via Range Coding
Ruiyi Yan, Yugo Murawaki
Linguistic steganography involves embedding secret messages within seemingly innocuous texts to enable covert communication. Provable security, which is a long-standing goal and ke…
Can We Trust LLM Detectors?
Jivnesh Sandhan, Harshit Jaiswal, Fei Cheng +1
The rapid adoption of LLMs has increased the need for reliable AI text detection, yet existing detectors often fail outside controlled benchmarks. We systematically evaluate 2 domi…
Persona Jailbreaking in Large Language Models
Jivnesh Sandhan, Fei Cheng, Tushar Sandhan +1
Large Language Models (LLMs) are increasingly deployed in domains such as education, mental health and customer support, where stable and consistent personas are critical for relia…
Language Lives in Sparse Dimensions: Toward Interpretable and Efficient Multilingual Control for Large Language Models
Chengzhi Zhong, Fei Cheng, Qianying Liu +3
Large language models exhibit strong multilingual capabilities despite limited exposure to non-English data. Prior studies show that English-centric large language models map multi…
How Does Cognitive Bias Affect Large Language Models? A Case Study on the Anchoring Effect in Price Negotiation Simulations
Yoshiki Takenami, Yin Jou Huang, Yugo Murawaki +1
Cognitive biases, well-studied in humans, can also be observed in LLMs, affecting their reliability in real-world applications. This paper investigates the anchoring effect in LLM-…
Addressing Tokenization Inconsistency in Steganography and Watermarking Based on Large Language Models
Ruiyi Yan, Yugo Murawaki
Large language models have significantly enhanced the capacities and efficiency of text generation. On the one hand, they have improved the quality of text-based steganography. On…
CAPE: Context-Aware Personality Evaluation Framework for Large Language Models
Jivnesh Sandhan, Fei Cheng, Tushar Sandhan +1
Psychometric tests, traditionally used to assess humans, are now being applied to Large Language Models (LLMs) to evaluate their behavioral traits. However, existing studies follow…
Investigating Cost-Efficiency of LLM-Generated Training Data for Conversational Semantic Frame Analysis
Shiho Matta, Yin Jou Huang, Fei Cheng +2
Recent studies have demonstrated that few-shot learning allows LLMs to generate training data for supervised models at a low cost. However, the quality of LLM-generated data may no…
Beyond English-Centric LLMs: What Language Do Multilingual Language Models Think in?
Chengzhi Zhong, Fei Cheng, Qianying Liu +5
In this study, we investigate whether non-English-centric LLMs, despite their strong performance, `think' in their respective dominant language: more precisely, `think' refers to h…
LLM-jp: A Cross-organizational Project for the Research and Development of Fully Open Japanese LLMs
LLM-jp, :, Akiko Aizawa +80
This paper introduces LLM-jp, a cross-organizational project for the research and development of Japanese large language models (LLMs). LLM-jp aims to develop open-source and stron…
Principal Component Analysis as a Sanity Check for Bayesian Phylolinguistic Reconstruction
Yugo Murawaki
Bayesian approaches to reconstructing the evolutionary history of languages rely on the tree model, which assumes that these languages descended from a common ancestor and underwen…
Addressing Segmentation Ambiguity in Neural Linguistic Steganography
Jumon Nozaki, Yugo Murawaki
Previous studies on neural linguistic steganography, except Ueoka et al. (2021), overlook the fact that the sender must detokenize cover texts to avoid arousing the eavesdropper's…
Frustratingly Easy Edit-based Linguistic Steganography with a Masked Language Model
Honai Ueoka, Yugo Murawaki, Sadao Kurohashi
With advances in neural language models, the focus of linguistic steganography has shifted from edit-based approaches to generation-based ones. While the latter's payload capacity…
A System for Worldwide COVID-19 Information Aggregation
Akiko Aizawa, Frederic Bergeron, Junjie Chen +26
The global pandemic of COVID-19 has made the public pay close attention to related news, covering various domains, such as sanitation, treatment, and effects on education. Meanwhil…
Minimally Supervised Learning of Affective Events Using Discourse Relations
Jun Saito, Yugo Murawaki, Sadao Kurohashi
Recognizing affective events that trigger positive or negative sentiment has a wide range of natural language processing applications but remains a challenging problem mainly becau…
On the Definition of Japanese Word
Yugo Murawaki
The annotation guidelines for Universal Dependencies (UD) stipulate that the basic units of dependency annotation are syntactic words, but it is not clear what are syntactic words…