LLM-NewsBench: Assessing Factuality, Hallucination, Headline Consistency, and Journalistic Quality in LLM-Generated News

Abstract

Purpose: Large language models (LLMs) are increasing capabilities for producing fluent, cohesive, and publishable news stories but language quality by itself is not an indication of factual correctness. This paper champions LLM-NewsBench, a source-grounded benchmark for factual evaluation of LLM-generated news articles against a human-authored reference corpus. Methodology: LLM-NewsBench comprises 50 news reports published by Dawn (Pakistan), each independently reconstructed three times from ChatGPT, Gemini, and DeepSeek. Both headlines and full news articles were evaluated. Headlines were scored for fidelity with the reference headline, while articles were scored for source-grounding completeness hallucination, journalistic style and neutrality, news article structure readability source fidelity, compression, and coherence. The proposed weighted compositional metric weighted factual (30%), completeness (20%), hallucination (20%), journalistic style (10%), structure (10%), and readability (10%) score yielded optimized aggregate benchmark scores of 0.87 for ChatGPT, 0.83 for Gemini, and 0.79 for DeepSeek (sets 150). Findings: ChatGPT also achieved the highest factual grounding neutrality source fidelity, and compression in the current evaluation corpus. Gemini produced more detailed and comprehensive reconstructions but proved more aggressive in editing expansions than the more conversative DeepSeek which produced more analytical and feature-rich reconstructions but posed a greater threat of unsupported add-ons and narrative drift. Headline analysis revealed reference matching headlines of 78% for ChatGPT, 70% for DeepSeek, and 48% for Gemini. Error analysis revealed made-up entities, fabricated events, unsupported data, time-disarticulation, location hijacking, and overcommitted causal expansion. Unique contribution to theory, practice and policy: These results demonstrate that headline similarity, semantic presentation, and stylistic quality are insufficient indicators of source-based fact complexity, and LLM-NewsBench establishes an operationalized reproducible multi-faceted computer-based evaluation infrastructure for news generation benchmarking, automated scoring, human validation, and visualization.