---
title: "On the application of Large Language Models for language"
description: "It does not introduce a single new resource. 133 references, cited by 18 papers here. Among them: 33."
authors: ["Andrew Caines", "Luca Benedetto", "Shiva Taslimipoor", "Christopher Davis", "Yuan Gao", "Oeistein Andersen", "Zheng Yuan", "Mark Elliott", "Russell Moore", "Christopher Bryant", "Marek Rei", "Helen Yannakoudakis", "Andrew Mullooly", "Diane Nicholls", "Paula Buttery"]
year: 2023
venue: "arXiv:2307.08393"
source: "https://arxiv.org/abs/2307.08393"
doi: "10.48550/arxiv.2307.08393"
arxiv: "2307.08393"
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/caines-2023-application-large-language-models-language/"
---
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# On the application of Large Language Models for language teaching and assessment technology

**Authors:** [Andrew Caines](https://md.research.elamur.ai/free/en/authors/andrew-caines/), [Luca Benedetto](https://md.research.elamur.ai/free/en/authors/luca-benedetto/), [Shiva Taslimipoor](https://md.research.elamur.ai/free/en/authors/shiva-taslimipoor/), [Christopher Davis](https://md.research.elamur.ai/free/en/authors/christopher-davis/), [Yuan Gao](https://md.research.elamur.ai/free/en/authors/yuan-gao/), Oeistein Andersen, [Zheng Yuan](https://md.research.elamur.ai/free/en/authors/zheng-yuan/), Mark Elliott, [Russell Moore](https://md.research.elamur.ai/free/en/authors/russell-moore/), [Christopher Bryant](https://md.research.elamur.ai/free/en/authors/christopher-bryant/), [Marek Rei](https://md.research.elamur.ai/free/en/authors/marek-rei/), [Helen Yannakoudakis](https://md.research.elamur.ai/free/en/authors/helen-yannakoudakis/), Andrew Mullooly, [Diane Nicholls](https://md.research.elamur.ai/free/en/authors/diane-nicholls/) and [Paula Buttery](https://md.research.elamur.ai/free/en/authors/paula-buttery/) · **Year:** 2023 · **Venue:** arXiv:2307.08393 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2307.08393) · [PDF](https://arxiv.org/pdf/2307.08393) · [DOI 10.48550/arxiv.2307.08393](https://doi.org/10.48550/arxiv.2307.08393) · [arXiv 2307.08393](https://arxiv.org/abs/2307.08393) · [OpenAlex](https://openalex.org/W4384648067)

**Citations:** 33 citations (OpenAlex, 2026-10-14) · [18](https://md.research.elamur.ai/free/en/papers/caines-2023-application-large-language-models-language/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
This position paper, from the ALTA Institute at Cambridge and partners, asks how very large language models such as GPT-4 and PaLM could support AI-based systems that teach and assess language learners. It does not introduce a single new resource. Instead, it surveys prior work and reports early experiments in three areas. The first is creating and calibrating learning content, such as exam items, cloze tests, distractors and simplified texts. The second is automated scoring of learner writing, and the third is grammatical error correction with feedback. The authors also review commercial products, proprietary and open model options, evaluation datasets, and ethical risks.

The overall verdict is cautious. LLMs make kinds of text generation feasible that were not practical before, but they need careful prompting and expert editing, ideally with a human in the loop. On benchmarked tasks like grading and error correction, LLMs alone do not yet surpass state-of-the-art systems. The work matters as a roadmap that tempers hype and specifies what evidence is still needed.

## Key points
- **Exploratory content study:** public GPT-3 models generated texts for low-stakes self-study exercises. Experts designed the prompts, polished the outputs and wrote the questions, which helps limit hallucination and offensive content.
- **Related work on content creation:**
  - Felice et al. placed cloze gaps using BERT and ELECTRA.
  - The UniHD team's GPT-3 system won the English track of the TSAR-2022 lexical simplification shared task.
- **Evaluation resources:**
  - Datasets: RACE, SCDE and CLOTH, plus the smaller CEPOC and Teacher-Student Chatroom Corpus, which suit zero- or few-shot testing.
  - Metrics: BLEU, ROUGE, METEOR and BERTScore.
- **Scoring and correction:** established linguistic features still give the best grading results. LLM-based error correction may produce valid feedback styles that current metrics fail to reward.
- **Deployment and conditions for adoption:**
  - Duolingo Max, built on GPT-4 (released March 2023), offers "Role Play" and "Explain My Answer", but only in some countries and languages.
  - The authors argue LLMs should be adopted only if three things hold. They must beat benchmarks by enough to justify their compute and environmental cost. They must benefit learners. They must not harm any group through bias or misinformation.

*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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🧭 Topics (1): [🧠 Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) · 3,320 works

## Cite

Cite as: Andrew Caines, Luca Benedetto, Shiva Taslimipoor, Christopher Davis, Yuan Gao, Oeistein Andersen, Zheng Yuan, Mark Elliott, Russell Moore, Christopher Bryant, Marek Rei, Helen Yannakoudakis, Andrew Mullooly, Diane Nicholls and Paula Buttery. 2023. On the application of Large Language Models for language teaching and assessment technology. arXiv:2307.08393.

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

```bibtex
@misc{caines2023application,
  title = {{On the application of Large Language Models for language teaching and assessment technology}},
  author = {Andrew Caines, Luca Benedetto, Shiva Taslimipoor, Christopher Davis, Yuan Gao, Oeistein Andersen, Zheng Yuan, Mark Elliott, Russell Moore, Christopher Bryant, Marek Rei, Helen Yannakoudakis, Andrew Mullooly, Diane Nicholls and Paula Buttery},
  year = {2023},
  doi = {10.48550/arxiv.2307.08393},
  eprint = {2307.08393},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2307.08393}
}
```

- [← previous Methods for Measuring, Updating, and Visualizing Factual Beliefs in Language Models](https://md.research.elamur.ai/free/en/papers/hase-2023-methods-measuring-updating-visualizing-factual/)
- [next → Self-Verification Improves Few-Shot Clinical Information Extraction](https://md.research.elamur.ai/free/en/papers/gero-2023-self-verification-improves-few-shot/)
- [↑ back to papers of 2023](https://md.research.elamur.ai/free/en/papers/2023/page/8/)

## References

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*Updated 1 November 2026*