some nlp datasets are someway really similar to schoolchildren exercises, did anybody compared the failures of humans vs ia? this could bring interesting insight on both
Studying the failures of natural language processing (NLP) models versus schoolchildren on question answering (QA) or part-of-speech (POS) tasks can provide valuable insights into the strengths and limitations of both humans and AI systems in language comprehension and processing.
On QA tasks, where models are tasked with answering questions based on provided text, comparing failures can reveal areas where NLP models struggle to understand context, infer meaning, or handle ambiguity. In contrast, analyzing schoolchildren’s mistakes can highlight common misunderstandings or challenges in interpreting written information, such as unfamiliar vocabulary or complex sentence structures.
Similarly, examining failures on POS tasks, which involve identifying the grammatical categories of words in a sentence, can uncover differences in the linguistic knowledge and processing abilities of NLP models and schoolchildren. For example, errors made by NLP models may stem from limitations in parsing syntactic structures or disambiguating homographs, while schoolchildren’s mistakes may reflect gaps in understanding grammar rules or applying them consistently.
Comparing the failures of NLP models and schoolchildren on QA and POS tasks can inform the development of more robust AI systems and educational strategies. By identifying common failure patterns and addressing underlying challenges, researchers can enhance NLP models’ performance and support students’ language learning and comprehension skills. Additionally, insights gained from these comparisons can contribute to advancing our understanding of human language processing and cognition.