2 citations · 3 across the 9 of their papers we have counts for
10 papers
Repeated Sequences Reveal Gaps between Large Language Models and Natural Language
Kumiko Tanaka-Ishii
Evaluating whether large language models (LLMs) capture the structure of natural language beyond local fluency remains an open challenge. Existing evaluation methods, largely based…
Escaping Mode Collapse in LLM Generation via Geometric Regulation
Xin Du, Kumiko Tanaka-Ishii
Mode collapse is a persistent challenge in generative modeling and appears in autoregressive text generation as behaviors ranging from explicit looping to gradual loss of diversity…
Artificial intelligence is creating a new global linguistic hierarchy
Giulia Occhini, Kumiko Tanaka-Ishii, Anna Barford +10
Artificial intelligence (AI) has the potential to transform healthcare, education, governance and socioeconomic equity, but its benefits remain concentrated in a small number of la…
Correlation Dimension of Auto-Regressive Large Language Models
Xin Du, Kumiko Tanaka-Ishii
Large language models (LLMs) have achieved remarkable progress in natural language generation, yet they continue to display puzzling behaviors -- such as repetition and incoherence…
Scale-free Characteristics of Multilingual Legal Texts and the Limitations of LLMs
Haoyang Chen, Kumiko Tanaka-Ishii
We present a comparative analysis of text complexity across domains using scale-free metrics. We quantify linguistic complexity via Heaps' exponent (vocabulary growth), Taylor'…
Understanding Benchmark Language Under Weakened Formal Semantics
Haoyang Chen, Kumiko Tanaka-Ishii
State-of-the-art NLP benchmarks require interpretation of natural language that specifies conditions, procedures, and exceptions, often relying on implicit assumptions and external…
Information-Theoretic Generative Clustering of Documents
Xin Du, Kumiko Tanaka-Ishii
We present {\em generative clustering} (GC) for clustering a set of documents, , by using texts generated by large language models (LLMs) instead of by clu…
Bottleneck-Minimal Indexing for Generative Document Retrieval
Xin Du, Lixin Xiu, Kumiko Tanaka-Ishii
We apply an information-theoretic perspective to reconsider generative document retrieval (GDR), in which a document is indexed by , and a neural autoregressive…
Correlation Dimension of Natural Language in a Statistical Manifold
Xin Du, Kumiko Tanaka-Ishii
The correlation dimension of natural language is measured by applying the Grassberger-Procaccia algorithm to high-dimensional sequences produced by a large-scale language model. Th…
Co-Training Realized Volatility Prediction Model with Neural Distributional Transformation
Xin Du, Kai Moriyama, Kumiko Tanaka-Ishii
This paper shows a novel machine learning model for realized volatility (RV) prediction using a normalizing flow, an invertible neural network. Since RV is known to be skewed and h…