Do Language Models Learn about Legal Entity Types during Pretraining?

• URL: http://arxiv.org/abs/2310.13092v1
• Date: Thu, 19 Oct 2023 18:47:21 GMT
• Title: Do Language Models Learn about Legal Entity Types during Pretraining?
• Authors: Claire Barale, Michael Rovatsos, Nehal Bhuta
• Abstract summary: We show that Llama2 performs well on certain entities and exhibits potential for substantial improvement with optimized prompt templates. Llama2 appears to frequently overlook syntactic cues, a shortcoming less present in BERT-based architectures.
• Score: 4.604003661048267
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Language Models (LMs) have proven their ability to acquire diverse linguistic knowledge during the pretraining phase, potentially serving as a valuable source of incidental supervision for downstream tasks. However, there has been limited research conducted on the retrieval of domain-specific knowledge, and specifically legal knowledge. We propose to explore the task of Entity Typing, serving as a proxy for evaluating legal knowledge as an essential aspect of text comprehension, and a foundational task to numerous downstream legal NLP applications. Through systematic evaluation and analysis and two types of prompting (cloze sentences and QA-based templates) and to clarify the nature of these acquired cues, we compare diverse types and lengths of entities both general and domain-specific entities, semantics or syntax signals, and different LM pretraining corpus (generic and legal-oriented) and architectures (encoder BERT-based and decoder-only with Llama2). We show that (1) Llama2 performs well on certain entities and exhibits potential for substantial improvement with optimized prompt templates, (2) law-oriented LMs show inconsistent performance, possibly due to variations in their training corpus, (3) LMs demonstrate the ability to type entities even in the case of multi-token entities, (4) all models struggle with entities belonging to sub-domains of the law (5) Llama2 appears to frequently overlook syntactic cues, a shortcoming less present in BERT-based architectures.

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