Rethinking the Evaluating Framework for Natural Language Understanding in AI Systems: Language Acquisition as a Core for Future Metrics

The paper discusses the need for a new approach to evaluate AI systems, particularly regarding natural language processing. It suggests moving away from the traditional Turing Test towards a framework based on language acquisition. This shift is inspired by the recent advancements in Large Language Models (LLMs). The authors argue that as AI systems become more integrated into our lives, the need for a robust and efficient evaluation method becomes more crucial. The paper aims to contribute to this discussion and propose a paradigm shift to meet the needs of the 21st century.

Publication date: 22 Sep 2023
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Paper: https://arxiv.org/pdf/2309.11981