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Representing symbolic knowledge as vectors: Lin et al. 2019) used BERT as the neural component to represent the instance (statement vector). 2019) fine-tuned a BERT model to solve the multiple choice questions. COMET has been used successfully in various downstream tasks requiring commonsense knowledge.
In the following example, for an LLM to answer the question correctly, it needs to understand the table row represents location and the column represents year, and then extract the correct quantity (total amount) from the table based on the asked location and year: Question : What was the Total Americas amount in 2019?
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