---
title: "Are Large Language Model-based Evaluators the Solution to"
description: "The authors test whether GPT-4, scoring outputs without reference texts, can replace or supplement… 35 references, cited by 62 papers here."
authors: ["Rishav Hada", "Varun Gumma", "Adrian de Wynter", "Harshita Diddee", "Mohamed Ahmed", "Monojit Choudhury", "Kalika Bali", "Sunayana Sitaram"]
year: 2023
venue: "arXiv:2309.07462"
source: "https://arxiv.org/abs/2309.07462"
doi: "10.48550/arxiv.2309.07462"
arxiv: "2309.07462"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/hada-2023-large-language-model-based-evaluators/"
---
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# Are Large Language Model-based Evaluators the Solution to Scaling Up Multilingual Evaluation?

**Authors:** [Rishav Hada](https://md.research.elamur.ai/free/en/authors/rishav-hada/), [Varun Gumma](https://md.research.elamur.ai/free/en/authors/varun-gumma/), Adrian de Wynter, [Harshita Diddee](https://md.research.elamur.ai/free/en/authors/harshita-diddee/), [Mohamed Ahmed](https://md.research.elamur.ai/free/en/authors/mohamed-ahmed/), [Monojit Choudhury](https://md.research.elamur.ai/free/en/authors/monojit-choudhury/), [Kalika Bali](https://md.research.elamur.ai/free/en/authors/kalika-bali/) and [Sunayana Sitaram](https://md.research.elamur.ai/free/en/authors/sunayana-sitaram/) · **Year:** 2023 · **Venue:** arXiv:2309.07462 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2309.07462) · [PDF](https://arxiv.org/pdf/2309.07462) · [DOI 10.48550/arxiv.2309.07462](https://doi.org/10.48550/arxiv.2309.07462) · [arXiv 2309.07462](https://arxiv.org/abs/2309.07462) · [OpenAlex](https://openalex.org/W4386794666)

**Citations:** 7 citations (OpenAlex, 2026-10-14) · [62](https://md.research.elamur.ai/free/en/papers/hada-2023-large-language-model-based-evaluators/cited-by/) in our library, of the 141,840 papers whose bibliographies we read

## Summary
Scaling LLM evaluation beyond English is difficult. Benchmarks cover few languages and few realistic tasks, automatic metrics handle rich morphology and unusual scripts poorly, and native-speaker judges are costly and slow. The authors test whether GPT-4, scoring outputs without reference texts, can replace or supplement human raters. They use outputs from a GPT-4-powered text generation product on three tasks (Open Prompt, Continue Writing, Summarize). The outputs come from high-resource languages ranging from English to Czech. The authors compare GPT-4's scores with roughly 20K native-speaker judgments across five quality dimensions and try several prompting setups. Raw agreement with humans looks high. However, GPT-4 consistently gives generous scores, especially when human raters disagree with each other. This gap is larger for non-Latin-script languages (Chinese, Japanese) and for the less-resourced Czech. The study warns against trusting LLM judges uncritically in multilingual settings. It recommends calibrating them against human labels in each language before use.

## Key points
- The five metrics are Linguistic Acceptability (LA), Output Content Quality (OCQ) and Task Quality (TQ), each scored 0–2, plus Problematic Content (PC) and Hallucinations (H), both binary.
- The abstract says eight languages, but the setup lists nine: En, Fr, De, Es, Zh, Ja, It, Pt-Br and Cs. Following Joshi et al. (2020), most are Class 5, and It, Pt-Br and Cs are Class 4.
- Each item received three annotations from contracted native speakers, paid about $14–30 USD/hr depending on locale.
- Scoring one metric per GPT-4 call worked better than scoring all metrics at once. Few-shot examples gave no improvement, and more detailed instructions did not remove the upward score bias.
- The human-annotated dataset is in-house and unreleased, which limits reproducibility. Code is available at https://aka.ms/LLM-Eval.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Rishav Hada, Varun Gumma, Adrian de Wynter, Harshita Diddee, Mohamed Ahmed, Monojit Choudhury, Kalika Bali and Sunayana Sitaram. 2023. Are Large Language Model-based Evaluators the Solution to Scaling Up Multilingual Evaluation?. arXiv:2309.07462.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{hada2023are,
  title = {{Are Large Language Model-based Evaluators the Solution to Scaling Up Multilingual Evaluation?}},
  author = {Rishav Hada, Varun Gumma, Adrian {de Wynter}, Harshita Diddee, Mohamed Ahmed, Monojit Choudhury, Kalika Bali and Sunayana Sitaram},
  year = {2023},
  doi = {10.48550/arxiv.2309.07462},
  eprint = {2309.07462},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2309.07462}
}
```

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*Updated 24 October 2026*