arXiv AI

GRACE: LLM-Grounded Semantic Metric Spaces for Scalable Mixed-Data Clustering

arXiv:2608. 07881v1 Announce Type: new Abstract: Clustering mixed tabular data requires a unified metric space to bridge the inherent heterogeneity between continuous numerical measurements and discrete categorical symbols.

arXiv AI

Sep 4

Mixed Data Clustering Survey and Challenges

The paper "Mixed Data Clustering Survey and Challenges" discusses how the rise of big data has made clustering of heterogeneous datasets—containing both numerical and categorical variables—particularly difficult for traditional methods. It highlights the importance of hierarchical and explainable algorithms for producing interpretable results that aid decision‑making. The authors propose a new clustering approach based on pretopological spaces and benchmark it against classical numerical clustering algorithms and existing pretopological methods to evaluate its performance in the big data context.

By Maxence Choufa, Clement Cornet, Guillaume Guerard, Sonia Djebali, Loup-No\'e Levy

benchmarks

arXiv AI

Jul 3

HERMES: A Multi-Granularity Labeling Substrate for Pre-training Data Mixtures

arXiv:2607. 02266v1 Announce Type: cross Abstract: Most data-mixing methods assume the corpus has already been partitioned into groups, and the choice of those groups determines what a mixer can express.

By Ziyun Qiao, Yue Min, Ruining Chen, Yujun Li

rag efficiency

arXiv AI

Jul 24

Interpretable Embeddings with Sparse Autoencoders: A Data Analysis Toolkit

arXiv:2512. 10092v2 Announce Type: replace Abstract: Analyzing large-scale text corpora is a core challenge in machine learning, crucial for tasks like identifying undesirable model behaviors or biases in training data.

By Nick Jiang, Xiaoqing Sun, Lisa Dunlap, Lewis Smith, Neel Nanda

llms rag

arXiv AI

Jun 3

ReaLM: Residual Quantization Bridging Knowledge Graph Embeddings and Large Language Models

arXiv:2510. 09711v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) have recently emerged as a powerful paradigm for Knowledge Graph Completion (KGC), offering strong reasoning and generalization capabilities beyond traditional embedding-based approaches.

By Wenbin Guo, Xin Wang, Jiaoyan Chen, Lingbing Guo, Zhao Li, Zirui Chen

llms rag nlp efficiency benchmarks

arXiv AI

Jul 7

Scalable Semantic Steering of Embedding Projections

arXiv:2607. 03978v1 Announce Type: cross Abstract: Low-dimensional projections support interactive visual analysis of high-dimensional data embeddings, but their structure often does not align with analyst-defined semantic relationships.

By Wei Liu, Eric Krokos, Kirsten Whitley, Rebecca Faust, Chris North

llms rag multimodal safety

arXiv AI

Jul 3

AgenticDataBench: A Comprehensive Benchmark for Data Agents

arXiv:2607. 01647v1 Announce Type: cross Abstract: Data science aims to derive actionable insights from heterogeneous raw data, unlocking the value of the massive amounts of data generated in modern society.

By Zhaoyan Sun, Shan Zhong, Daizhou Wen, Jiaxing Han, Guoliang Li, Ying Yan, Peng Zhang, Yu Su, Xiang Qi, Baolin Sun, Chengyuan Yang, Tao Fang, Huaiyu Ruan

llms agents benchmarks

arXiv AI

Aug 5

Enhancing Tabular Learners with Context-Aware Semantic Embeddings

arXiv:2608. 03565v1 Announce Type: new Abstract: While modern tabular learners excel at capturing statistical patterns, they frequently operate in a semantic vacuum, treating textual features as discrete symbols, ignoring the rich semantics inherent in feature names or cell entries.

By G\"unther Schindler, Maximilian Schambach, Johannes H\"ohne

llms rag efficiency benchmarks

arXiv Machine Learning

Jun 30

TextClusterLab: An Integrated Framework for Reliable Text Clustering Studies

arXiv:2606. 28328v1 Announce Type: cross Abstract: In recent years, text clustering has become a critical technique for applications including intent discovery, topic mining, and recommendation systems.

By Daoming Wan, Yizheng Huang, Jimmy X. Huang

llms rag benchmarks

arXiv Machine Learning

Jun 30

Personalized Additive Modeling for Multi-level Federated Learning

arXiv:2405. 16472v2 Announce Type: replace Abstract: Contemporary AI faces the challenge of balancing generality with user-specific personalization.

By Shutong Chen, Guodong Long, Tianyi Zhou, Jie Ma, Jing Jiang, Chengqi Zhang

arXiv AI

Jun 12

scLLM-DSC: LLM-Knowledge Enhanced Cross-Modal Deep Structural Clustering for Single-Cell RNA Sequencing

arXiv:2606. 13007v1 Announce Type: cross Abstract: Clustering is fundamental to scRNA-seq analysis, serving as a cornerstone for identifying cell populations and resolving tissue heterogeneity.

By Ping Xu, Pengjiang Li, Tian Du, Zaitian Wang, Jiawei Gu, Ziyue Qiao, Pengfei Wang, Yuanchun Zhou

llms rag diffusion multimodal benchmarks safety

arXiv Computation and Language

Sep 23

Semantic Abstraction for Natural Language Inference: a Methodological Framework for Discovering and Compensating Semantic Knowledge and Reasoning Gaps in Large Language Models

arXiv:2609.26610v1 Announce Type: new Abstract: Despite their outstanding performance on many NLP tasks, LLMs face serious challenges related to semantic abstraction. In this study, we are interested...

By David Torres-Moreno, Jorge Hermosillo-Valadez

llms agents nlp

arXiv AI

Aug 28

Grounded Token Initialization for New Vocabulary in LMs for Generative Recommendation

The paper examines how new vocabulary tokens are added to language models for generative recommendation tasks. It shows that the common practice of initializing these tokens as the mean of existing embeddings collapses them into a degenerate subspace, hindering fine‑tuning. The authors propose Grounded Token Initialization (GTI), which places new tokens at semantically meaningful positions in the pretrained embedding space using linguistic supervision, and demonstrate that GTI outperforms mean initialization and other adaptation methods across several benchmarks.

By Daiwei Chen, Zhoutong Fu, Chengming Jiang, Haichao Zhang, Ran Zhou, Tan Wang, Chunnan Yao, Guoyao Li, Rui Cai, Yihan Cao, Ruijie Jiang, Fedor Borisyuk, Jianqiang Shen, Jingwei Wu, Ramya Korlakai Vinayak

llms rag fine-tuning benchmarks

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