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nlg evaluation

Evaluate the generated text by NLG (Natural Language Generation) systems, like large language models

Papers

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Showing 26–50 of 71 papers

TitleStatusHype
DEBATE: Devil's Advocate-Based Assessment and Text EvaluationCode0
WaterJudge: Quality-Detection Trade-off when Watermarking Large Language Models—0
Are LLM-based Evaluators Confusing NLG Quality Criteria?Code0
One Prompt To Rule Them All: LLMs for Opinion Summary EvaluationCode0
LLM-based NLG Evaluation: Current Status and Challenges—0
The Pitfalls of Defining Hallucination—0
CoAScore: Chain-of-Aspects Prompting for NLG Evaluation—0
X-Eval: Generalizable Multi-aspect Text Evaluation via Augmented Instruction Tuning with Auxiliary Evaluation Aspects—0
Towards Multiple References Era -- Addressing Data Leakage and Limited Reference Diversity in NLG EvaluationCode0
LLM Comparative Assessment: Zero-shot NLG Evaluation through Pairwise Comparisons using Large Language ModelsCode0
DecompEval: Evaluating Generated Texts as Unsupervised Decomposed Question AnsweringCode0
Rethinking Model Evaluation as Narrowing the Socio-Technical Gap—0
Dolphin: A Challenging and Diverse Benchmark for Arabic NLG—0
Not All Metrics Are Guilty: Improving NLG Evaluation by Diversifying ReferencesCode0
Describe me an Aucklet: Generating Grounded Perceptual Category DescriptionsCode0
CLSE: Corpus of Linguistically Significant EntitiesCode0
Dialect-robust Evaluation of Generated Text—0
NLG-Metricverse: An End-to-End Library for Evaluating Natural Language Generation—0
EffEval: A Comprehensive Evaluation of Efficiency for MT Evaluation MetricsCode0
A Dynamic, Interpreted CheckList for Meaning-oriented NLG Metric Evaluation – through the Lens of Semantic Similarity Rating—0
A Dynamic, Interpreted CheckList for Meaning-oriented NLG Metric Evaluation -- through the Lens of Semantic Similarity Rating—0
The Authenticity Gap in Human Evaluation—0
Near-Negative Distinction: Giving a Second Life to Human Evaluation DatasetsCode0
Deconstructing NLG Evaluation: Evaluation Practices, Assumptions, and Their Implications—0
Bridging Cross-Lingual Gaps During Leveraging the Multilingual Sequence-to-Sequence Pretraining for Text Generation and UnderstandingCode0
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