2 citations · 3 across the 6 of their papers we have counts for
6 papers
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…
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…
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…