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.
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...
Muiz Mir Ayesha, Abbas Rashid Butt· International Journal of Com...· 0 citations
This work introduces MISHAP-Bench, a comprehensive benchmark with 12,000 challenging open-ended question-audio pairs and a rigorous evaluation pipeline covering two hallucination categories, and proposes a groundedness judge that uses reference rubrics and judge prompts guided by human annotations.
Wen-Soi Zhi, Giulio Segalini, Jian-Jia Chen et al.· 0 citations
Large language models (LLMs) have come into widespread use in recent years across domains ranging from education and journalism to academic research and everyday information seeking. Their ability to produce fluent, coherent-sounding answers in natural language creates the impression that these answers are accurate and...
Harun Nabiyev· Aposta: Revista de Ciencias...· 0 citations
NovGauge is presented, a human-anchored benchmark for fine-grained novelty assessment diagnosis, and a cascading diagnostic pipeline that verifies per-dimension correctness, evidence grounding, and logical support is proposed.
Guo-Qiang Zhang, Ke-Xin Tan, Ming Zhang et al.· 0 citations
EviSI, a large language model evaluation agent combining Multidimensional Quality Metrics (MQM) with criteria developed with professional interpreters, is proposed, showing positive system ranking correlations with COMET throughout.
Ben Yan, Zong-Yao Li, Xiao-Yu Chen et al.· 0 citations
A multi-signal detection pipeline combining fine-tuned DeBERTa-v3 classification, Monte Carlo Dropout uncertainty quantification, and temperature-scaled calibration for response-level hallucination detection is presented.
Varun Teja Chundru, Debasmita Biswas· 0 citations
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