Altigran da Silva

Marina Soares Marinho, Daniela Vianna, Livy Real et al.

This study presents the Jusbrasil Study on the Use of General-Purpose AIs in Law, proposing an experimental evaluation protocol combining legal theory, such as material correctness, systematic coherence, and argumentative integrity, with empirical assessment by 48 legal professionals. Four systems (JusIA, ChatGPT Free, ChatGPT Pro, and Gemini) were tested in tasks simulating lawyers' daily work. JusIA, a domain-specialized model, consistently outperformed the general-purpose systems, showing that both domain specialization and a theoretically grounded evaluation are essential for reliable legal AI outputs.

Lucas R. C. Pessutto, Tiago de Melo, Viviane P. Moreira et al.

This paper describes the system submitted by our team (BabelEnconding) to SemEval-2020 Task 3: Predicting the Graded Effect of Context in Word Similarity. We propose an approach that relies on translation and multilingual language models in order to compute the contextual similarity between pairs of words. Our hypothesis is that evidence from additional languages can leverage the correlation with the human generated scores. BabelEnconding was applied to both subtasks and ranked among the top-3 in six out of eight task/language combinations and was the highest scoring system three times.