mlsanityData to 13 Oct 2026

Towards Neural Language Evaluators

arXiv 1909.09268 ↗First posted 20 Sep 2019cs.CL cs.AI cs.LGcited by 3

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Abstract (arXiv)

We review three limitations of BLEU and ROUGE -- the most popular metrics used to assess reference summaries against hypothesis summaries, come up with criteria for what a good metric should behave like and propose concrete ways to use recent Transformers-based Language Models to assess reference summaries against hypothesis summaries.

Key facts. Cited by 3 papers in the map (data through 13 Oct 2026). First posted on arXiv 20 Sep 2019.

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Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 0; 2026: 1.

3 papers.

  1. 2022Improving abstractive summarization with energy-based re-rankingcited by 3
  2. 2024SaGE: Evaluating Moral Consistency in Large Language Modelscited by 3
  3. 2026Automated Summarization of Software Documents: An LLM-based Multi-Agent Approachcited by 1