arXiv subjects

John Talburt

Publications and source records attributed to John Talburt.

4 recordsLinked to original sources

arXiv2026

Evaluating Semantic and Syntactic Understanding in Large Language Models for Payroll Systems

Large language models are now used daily for writing, search, and analysis, and their natural language understanding continues to improve. However, they remain unreliable on exact numerical calculation and on producing outputs that are straightforward to audit. We study synthetic payroll system as a focused, high-stakes example and evaluate whether models can understand a payroll schema, apply rules in the right order, and deliver cent-accurate results. Our experiments span a tiered dataset from basic to complex cases, a spectrum of prompts from minimal baselines to schema-guided and reasoning variants, and multiple model families including GPT, Claude, Perplexity, Grok and Gemini. Results indicate clear regimes where careful prompting is sufficient and regimes where explicit computation is required. The work offers a compact, reproducible framework and practical guidance for deploying LLMs in settings that demand both accuracy and assurance.

cs.CL↗

arXiv2026

A System for Name and Address Parsing with Large Language Models

Reliable transformation of unstructured person and address text into structured data remains a key challenge in large-scale information systems. Traditional rule-based and probabilistic approaches perform well on clean inputs but fail under noisy or multilingual conditions, while neural and large language models (LLMs) often lack deterministic control and reproducibility. This paper introduces a prompt-driven, validation-centered framework that converts free-text records into a consistent 17-field schema without fine-tuning. The method integrates input normalisation, structured prompting, constrained decoding, and strict rule-based validation under fixed experimental settings to ensure reproducibility. Evaluations on heterogeneous real-world address data show high field-level accuracy, strong schema adherence, and stable confidence calibration. The results demonstrate that combining deterministic validation with generative prompting provides a robust, interpretable, and scalable solution for structured information extraction, offering a practical alternative to training-heavy or domain-specific models.

cs.CL↗

arXiv2026

AI-Powered Multi-Stakeholder Ecosystems for Global Development: A Design Research Study on the GSI D-Hub Proof-of-Concept Platform

Digital platforms increasingly support collaboration across organizations, yet many remain constrained by fragmented data and limited transparency. This paper presents the Global Solutions Initiative (GSI) D-Hub, a data-driven coordination platform that applies explainable artificial intelligence (AI) for transparent matchmaking among deployers, solution providers, and financiers. The system integrates structured data models, interpretable algorithms, and synthetic data pipelines to reduce information asymmetries and improve data quality. Using a design-science approach, the platform was developed and validated with stakeholders from development, technology, and finance sectors. Results show that explainable recommendations and contextual dashboards enhance trust, usability, and decision confidence. The study contributes to data mining and data governance research by demonstrating how explainable, verifiable algorithms can enable scalable, trustworthy digital ecosystems for public collaboration.

cs.HC↗

arXiv2024

Towards Trustable Language Models: Investigating Information Quality of Large Language Models

Large language models (LLM) are generating information at a rapid pace, requiring users to increasingly rely and trust the data. Despite remarkable advances of LLM, Information generated by LLM is not completely trustworthy, due to challenges in information quality. Specifically, integrity of Information quality decreases due to unreliable, biased, tokenization during pre-training of LLM. Moreover, due to decreased information quality issues, has led towards hallucination, fabricated information. Unreliable information can lead towards flawed decisions in businesses, which impacts economic activity. In this work, we introduce novel mathematical information quality evaluation of LLM, we furthermore analyze and highlight information quality challenges, scaling laws to systematically scale language models.

cs.CL↗