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README.md

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 **Introduction**
 
-In traditional knowledge base question answering (KBQA) methods, semantic parsing plays a crucial role. It requires requires a semantic parser to be extensively trained on a vast dataset of labeled examples, typically consisting of question-answer or question-program pairs. This necessity arises primarily because smaller models before the advent of large language models (LLMs) were data-hungry, needing extensive data to effectively master tasks. Additionally, these methods often relied on the assumption that data is independent and identically distributed (i.i.d.), meaning the questions a model could answer needed to match the distribution of the training data. This demanded training data to cover a broad spectrum of the KB's entities and relationships for the model to understand it adequately.
+In traditional knowledge base question answering (KBQA) methods, semantic parsing plays a crucial role. It requires a semantic parser to be extensively trained on a vast dataset of labeled examples, typically consisting of question-answer or question-program pairs. 
 
 However, the rise of LLMs has shifted this paradigm. LLMs excel in learning from few (or even zero) in-context examples. They utilize natural language as a general vehicle of thought, enabling them to actively navigate and interact with KBs using auxiliary tools, without the need for training on comprehensive datasets. This advance suggests LLMs can sidestep the earlier limitations and eliminate the dependency on extensive, high-coverage training data.