Getting Logic From LLMs: Annotating Natural Language Inference with Sabiá - Avais, Fabiana; Carreira, Marcos; Real, Livy (Anais do Simpósio Brasileiro de Tecnologia da Informação e da Linguagem Humana (STIL), 2024)
We discuss the difficulties of annotation for Natural Language Inference in Portuguese, comparing human and Large Language Model annotations. We used 200 sentence pairs from the ASSIN2 dataset and re-annotated them for the inference task. A semanticist conducted the first annotation, and a second round was conducted using Sabiá-3, a large language model trained on Brazilian Portuguese data. We found that Sabiá-3 has the same agreement score as human annotators, but the LLM and human annotators disagree in cases involving different linguistic phenomena. While humans tend to disagree on pairs involving pragmatics or cultural knowledge, Sabiá-3 tends to mislabel sentences that share context but with no clear, logical relations among them. It shows that although LLMs are now statistically as effective as humans, LLMs and humans have different patterns for disagreement or mistaken annotations for Natural Language Inference.