arXiv Machine Learning

Machine Learning for Coding Retail Product Names to Consumer-Price Categories: A Rule-plus-Bag-of-Words Pipeline with Reliability-Weighted Human-in-the-Loop Labeling

arXiv:2606. 02004v1 Announce Type: cross Abstract: Consumer-price measurement increasingly draws on alternative data sources -- scanner, web-scraped, and transaction/receipt data.

arXiv AI

Jun 24

Evaluating LLM Usage for Efficient and Explainable Numerical and Classified Implicit Sentiment Analysis of Product Desirability

arXiv:2606. 23701v1 Announce Type: cross Abstract: Qualitative product feedback can reveal nuanced user experiences, but its implicit sentiment is difficult to measure.

By Sherri Weitl-Harms, John Hastings

llms nlp safety

arXiv AI

Jun 4

Rethinking Sales Lead Scoring with LLM-based Hierarchical Preference Ranking

arXiv:2606. 04387v1 Announce Type: cross Abstract: Sales lead conversion in high-stakes domains (e.

By Chenyu Zhang, Yiwen Liu, Yin Sun, Xinyuan Zhang, Yuji Cao, Junming Jiao, Juyi Qiao

llms benchmarks safety

arXiv AI

Jul 13

DaDaDa: A Dataset for Data Pricing in Data Marketplaces

arXiv:2607. 08785v1 Announce Type: cross Abstract: High-quality data drives machine learning advances across industries.

By Qiheng Sun, Hongwei Zhang, Junxu Liu, Xiaokai Mao, Jinfei Liu, Kui Ren, Haibo Hu

benchmarks

arXiv AI

Jul 14

Constraint-Aware Hierarchical Search for Regulation-Driven Fine-Grained Classification

arXiv:2607. 10588v1 Announce Type: new Abstract: Tasks such as customs tariff classification, export control categorization, and standards-based equipment coding require assigning an input instance to a fine-grained class under an explicit regulatory hierarchy.

By Siyu Wang, Wei Tan, Lulu Chen

llms rag nlp benchmarks

arXiv Machine Learning

Jul 22

A Classifier That Teaches Itself: Self-Improving, Frozen-gate Training (SIFT) for Dynamic Document Classification

arXiv:2607. 18358v1 Announce Type: cross Abstract: Document classification is a solved problem in the laboratory and an unsolved one in the enterprise.

By Bogdan Raduta, Horia Velicu, Alexandru Preda, Serban Chiricescu

llms safety

arXiv Machine Learning

1d ago

Discrete Diffusion Language Models Are Training-Free Multi-Label Classifiers

arXiv:2608. 14649v1 Announce Type: new Abstract: We present dLLM-SetScore, a training-free method that uses discrete masked-diffusion language models for multi-label text classification.

By Pawan Kumar

llms diffusion nlp fine-tuning

Hugging Face Trending Papers

1d ago

Where A Small Language Model Helps in Invoice Categorisation, Understood Through Embedding Geometry

Categorising invoices into the correct General Ledger (GL) code underpins financial reporting and tax compliance. This is a skilled accounting judgement rather than a routine task: the correct category depends subtly on the nature of the purchasing business, the vendor and the invoice text.

llms rag fine-tuning safety

arXiv AI

Jul 9

Large Behavior Model: A Promptable Digital Twin of the Retail Customer

arXiv:2607. 06993v1 Announce Type: new Abstract: Customer behavior modeling underpins recommendation, marketing, and decision support, yet existing approaches either optimize predictive accuracy without explaining decisions or simulate users without grounding them in real behavioral data.

By Wachiravit Modecrua, Krittin Pachtrachai, Touchapon Kraisingkorn

llms rag reinforcement-learning fine-tuning benchmarks

arXiv Machine Learning

22h ago

Where A Small Language Model Helps in Invoice Categorisation, Understood Through Embedding Geometry

arXiv:2608. 18033v1 Announce Type: cross Abstract: Categorising invoices into the correct General Ledger (GL) code underpins financial reporting and tax compliance.

By Emma Ceccherini, Daniel Lawson, Anjulika Salhan

llms rag fine-tuning safety

arXiv AI

Jul 31

Diagnosing Fine-Grained Inconsistency Classification in Financial Disclosure Text

arXiv:2607. 26368v1 Announce Type: cross Abstract: Financial disclosures contain numerical claims, temporal statements, entity references, policy commitments, and risk descriptions that may conflict in qualitatively different ways.

By Aman Kumar, Lasitha Vidyaratne, Dipanjan D Ghosh, Arnab Chakrabarti, Ahmed K Farahat

llms rag fine-tuning benchmarks

Hugging Face Trending Papers

Aug 4

SeqLLM: Augmenting LLMs with Behavioral-Sequence Modeling for High-Stakes Decisions at WeChat Pay

Merchant risk control at large payment platforms screens tens of millions of merchants daily, where false positives harm legitimate merchants and false negatives leave harmful activity undetected. The hardest cases require jointly understanding a merchant's textual profile and long behavioral sequence.

llms rag fine-tuning benchmarks safety

Hugging Face Trending Papers

Jul 28

SPARC Segmentation to Prediction via Affine Regression and Counterfactuals

Transaction propensity prediction in B2B e commerce presents unique challenges distinct from B2C contexts, primarily due to the heterogeneous procurement behaviors of organizational entities, which violate SMOTE's implicit assumption of within class feature homogeneity. Specifically, B2B buyers exhibit multi modal procurement cycles that render linear interpolation between minority class samples structurally invalid, producing synthetic data that does not represent real purchasing behavior.

computer-vision

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