Abstract
Computational models of legal reasoning often employ factors to reason about cases. Factors can be used to analogize a current factual scenario and precedents and to make arguments for or against a conclusion. Courts not only determine whether a factor applies to a case or not, but often how strongly the factor applies, that is, the factor’s magnitude in the case. Previous methods for automatically extracting factors from cases cannot identify factors’ magnitudes. We present and evaluate a method employing Large Language Models (LLMs) to identify factor magnitudes using few-shot prompts with or without Wordnet definitions. We also show how the extracted magnitudes can be used in constructing legal arguments that employ factors and magnitudes the way judges and lawyers do.