large language models

Let Me Explain!: Explainable NLP for Understanding Large Language Models

Large Language Models (LLMs) have transformed natural language processing (NLP), but their billions of parameters make them opaque. This lack of transparency is especially problematic in high-risk areas such as healthcare, finance, and content moderation, where understanding model decisions is essential for responsible...

Industrial anomaly detection via RGB-event fusion with multimodal large language models

In complex industrial scenarios, traditional Industrial Anomaly Detection methods often perform poorly under extreme conditions such as low light, high-speed motion, and strong light interference. To address this, we propose RGB-Event Industrial Anomaly Detection via Vision-Language Model (ReIAD-VL), integrating RGB im...

data for Evaluative Cultural Hallucination in Large Language Model Assessment of Tenor in Scenic Area Public Sign Translations

IWAC AI Pipelines

Python pipelines that apply large language models to the curation of a digital archive: OCR extraction and correction, summarization, named-entity recognition with authority reconciliation, audio and video transcription, handwritten text recognition, and sentiment analysis, all written back to an Omeka S instance. Buil...

From Data Quality to Quality of Agentic Data Use: A Conceptual Framework for Agentic Data Engineering

A conceptual framework for Agentic Data Engineering centered on Quality of Agentic Data Use, defined as the extent to which an agent uses and communicates data in accordance with task, semantic, quality, security, governance, and provenance requirements, is developed.

Comparative performance of ChatGPT-4.0, DeepSeek, Gemini and Perplexity in answering common questions from patients with COPD

This study compared responses from the LLMs ChatGPT-4.0, DeepSeek, Gemini, and Perplexity to 20 English-language questions about COPD that patients could present and found no LLM consistently met the approximate eighth-grade target.

Design and Validation of a Multimodal AI Conversational System for Automated Travel Itinerary Generation and Promotional Video Synthesis

The manual creation of personalized travel itineraries remains a labor-intensive process that requires travel agents to consolidate heterogeneous information from multiple sources, including natural language interactions, booking confirmations, screenshots, and reservation documents. Although recent advances in multimo...

AnTenA: Actionable and Explainable Tensor Analysis System with Large Language Models

Music Playlist Captioning at Scale with Large Language Models

Scaling Laws Do Not Predict Adversarial Robustness in LLM-Based Security Detection Systems

Large language models (LLMs) are increasingly deployed as automateddefenders in security operations, generating detection rules from attacktelemetry. This paper originally claimed that model size does not predictadversarial detection robustness, based on benchmarking five open-weightmodels (3.8B-14B parameters) with DA...

Artifacts of "Grounding Large Language Models in a Formal Symbolic Environment for Natural-Language Planning"

Experimental results for the paper "Grounding Large Language Models in a Symbolic Formalization Environment for Natural-Language Planning". The archive contains the results of 5,100 formalization sessions. Qwen 3.5 (122B) and GPT-OSS (120B) each formalized the 2,550 Blocksworld, Logistics and Rovers problems of the ben...

SADE: Symptom-Aware Diagnostic Escalation for LLM-Based Network Troubleshooting

Large language model (LLM) agents are increasingly applied to network troubleshooting, but root-cause localization on public benchmarks remains well below practical deployment thresholds. We argue this is because existing agents do not encode the disciplined, layer-by-layer methodology that human network engineers use,...