WSC+: Enhancing The Winograd Schema Challenge Using Tree-of-Experts
CoRR(2024)
摘要
The Winograd Schema Challenge (WSC) serves as a prominent benchmark for
evaluating machine understanding. While Large Language Models (LLMs) excel at
answering WSC questions, their ability to generate such questions remains less
explored. In this work, we propose Tree-of-Experts (ToE), a novel prompting
method which enhances the generation of WSC instances (50
in recent methods). Using this approach, we introduce WSC+, a novel dataset
comprising 3,026 LLM-generated sentences. Notably, we extend the WSC framework
by incorporating new 'ambiguous' and 'offensive' categories, providing a deeper
insight into model overconfidence and bias. Our analysis reveals nuances in
generation-evaluation consistency, suggesting that LLMs may not always
outperform in evaluating their own generated questions when compared to those
crafted by other models. On WSC+, GPT-4, the top-performing LLM, achieves an
accuracy of 68.7
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要