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

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 $exact\ F_1$ 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 $exact\ F_1$ 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.

@article{góngora2025_2506.11243,
  title={ RETUYT-INCO at BEA 2025 Shared Task: How Far Can Lightweight Models Go in AI-powered Tutor Evaluation? },
  author={ Santiago Góngora and Ignacio Sastre and Santiago Robaina and Ignacio Remersaro and Luis Chiruzzo and Aiala Rosá },
  journal={arXiv preprint arXiv:2506.11243},
  year={ 2025 }
}