# RETUYT-INCO at BEA 2025 Shared Task: How Far Can Lightweight Models Go in AI-powered Tutor Evaluation?

arXiv: https://arxiv.org/abs/2506.11243 · HTML: https://mlsanity.com/paper/2506.11243/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 17 references that are in the map.
- First posted on arXiv 12 Jun 2025.

## Builds on (most cited first, 8 of 8)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Unsupervised Cross-lingual Representation Learning at Scale](https://mlsanity.com/paper/1911.02116/) (2019)
- [Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model](https://mlsanity.com/paper/2211.02001/) (2022)
- [Unifying AI Tutor Evaluation: An Evaluation Taxonomy for Pedagogical Ability Assessment of LLM-Powered AI Tutors](https://mlsanity.com/paper/2412.09416/) (2024)
- [The BEA 2023 Shared Task on Generating AI Teacher Responses in Educational Dialogues](https://mlsanity.com/paper/2306.06941/) (2023)
- [Green AI: Exploring Carbon Footprints, Mitigation Strategies, and Trade Offs in Large Language Model Training](https://mlsanity.com/paper/2404.01157/) (2024)

## Built on by (most cited first, 2 of 2)

- [Findings of the BEA 2025 Shared Task on Pedagogical Ability Assessment of AI-powered Tutors](https://mlsanity.com/paper/2507.10579/) (2025)
- [RETUYT-INCO at BEA 2026 Shared Task 2: Meta-prompting in Rubric-based Scoring for German](https://mlsanity.com/paper/2605.11242/) (2026)

## Abstract (arXiv)

In this paper, we present the RETUYT-INCO participation at the BEA 2025 shared task. Our participation was characterized by the decision of using relatively small models, with fewer than 1B parameters. This self-imposed restriction tries to represent the conditions in which many research labs or institutions are in the Global South, where computational power is not easily accessible due to its prohibitive cost. Even under this restrictive self-imposed setting, our models managed to stay competitive with the rest of teams that participated in the shared task. According to the exact F1 scores published by the organizers, the performance gaps between our models and the winners were as follows: 6.46 in Track 1; 10.24 in Track 2; 7.85 in Track 3; 9.56 in Track 4; and 13.13 in Track 5. Considering that the minimum difference with a winner team is 6.46 points -- and the maximum difference is 13.13 -- according to the exact F1 score, we find that models with a size smaller than 1B parameters are competitive for these tasks, all of which can be run on computers with a low-budget GPU or even without a GPU.