CASE: Commonsense-Augmented Score with an Expanded Answer Space
Wenkai Chen,
Sahithya Ravi,
Vered Shwartz
Abstract:LLMs have demonstrated impressive zero-shot performance on NLP tasks thanks to the knowledge they acquired in their training. In multiplechoice QA tasks, the LM probabilities are used as an imperfect measure of the plausibility of each answer choice. One of the major limitations of the basic score is that it treats all words as equally important. We propose CASE, a Commonsense-Augmented Score with an Expanded Answer Space. CASE addresses this limitation by assigning importance weights for individual words base… Show more
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