Our objective for Task 8 at Semeval-2024 was to compete with large-scale solutions with models that could run on commonplace systems like mid-range laptops. For this purpose, we discarded LLM-based solutions that are prevalent in the related work in the field, opting instead for LSTMs and MLPs whose size can more easily be controlled. 

While our team did manage to keep model size under control (none of the proposed solutions require more than 1 GB of memory), the systems we proposed performed less than ideally in the task itself. On both subtasks we participated in, our best models failed to match the transformer baseline in the test set, despite positive results in the development set. 

Despite the final results, we believe our approach to be valid. Our development processes likely ended up producing models that were overly tuned to the development set. With more time, it would be possible to produce more refined and generalizable solutions. Trying different training strategies, like contrastive learning, or different architectures, such as mixture-of-experts systems, might be a good direction to follow in future work.

To most problems that arise in the field of NLP, researchers and companies increasingly respond with huge models that require dedicated servers to run. But for many users, keeping their data safely on their own machines is a priority, thus discarding many of the contemporary LLM-based services. System designers should aim to strike a compromise between size and performance, and prioritise users being able to own their workflows when possible. Like our attempt did for this shared task, we believe researches should consider these objectives when proposing new solutions.