# Towards Neural Language Evaluators

arXiv: https://arxiv.org/abs/1909.09268 · HTML: https://mlsanity.com/paper/1909.09268/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2019.

## Built on by (most cited first, 3 of 3)

- [Improving abstractive summarization with energy-based re-ranking](https://mlsanity.com/paper/2210.15553/) (2022)
- [SaGE: Evaluating Moral Consistency in Large Language Models](https://mlsanity.com/paper/2402.13709/) (2024)
- [Automated Summarization of Software Documents: An LLM-based Multi-Agent Approach](https://mlsanity.com/paper/2606.24689/) (2026)

## 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.