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原文連結
論文資訊
- 類型：已發表論文
- 日期：2024
摘要
Large 語言 models (LLMs) now perform extremely well on many natural 語言 processing tasks. Their ability to convert legal texts to data may offer empirical legal studies (ELS) scholars a low-cost alternative to research assistants in many contexts. However, less complex 計算 語言 models, such as topic modeling and sentiment analysis, are more interpretable than LLMs. In this paper we highlight these differences by comparing LLMs with less complex models on three ELS-related tasks. Our findings suggest that ELS research will - for the time being - benefit from combining LLMs with other techniques to optimize the strengths of each approach.
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