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Found 2 papers, 2 papers with code
Truth or Mirage? Towards End-to-End Factuality Evaluation with LLM-Oasis
After the introduction of Large Language Models (LLMs), there have been substantial improvements in the performance of Natural Language Generation (NLG) tasks, including Text Summarization and Machine Translation.
FENICE: Factuality Evaluation of summarization based on Natural language Inference and Claim Extraction
To address these shortcomings, we propose Factuality Evaluation of summarization based on Natural language Inference and Claim Extraction (FENICE), a more interpretable and efficient factuality-oriented metric.
Ranked #1 on
Summarization Consistency Evaluation
on AggreFact
Natural Language Inference
Summarization Consistency Evaluation
