LeXFiles and LegalLAMA: Facilitating English Multinational Legal Language Model Development ⚖️ 👩‍⚖️ 🗺️

Introduction

Citation

Ilias Chalkidis*, Nicolas Garneau*, Catalina E.C. Goanta, Daniel Martin Katz, and Anders Søgaard. LeXFiles and LegalLAMA: Facilitating English Multinational Legal Language Model Development. 2022. In the Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics. Toronto, Canada.

@inproceedings{chalkidis-etal-2023-lexfiles,
    title = "{L}e{XF}iles and {L}egal{LAMA}: Facilitating {E}nglish Multinational Legal Language Model Development",
    author = "Chalkidis, Ilias  and
      Garneau, Nicolas  and
      Goanta, Catalina  and
      Katz, Daniel  and
      S{\o}gaard, Anders",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.865",
    pages = "15513--15535"
}

The LexFiles Corpus

The LeXFiles is a new diverse English multinational legal corpus that we created including 11 distinct sub-corpora that cover legislation and case law from 6 primarily English-speaking legal systems (EU, CoE, Canada, US, UK, India). The corpus contains approx. 19 billion tokens. In comparison, the "Pile of Law" corpus released by Hendersons et al. (2022) comprises 32 billion in total, where the majority (26/30) of sub-corpora come from the United States of America (USA), hence the corpus as a whole is biased towards the US legal system in general, and the federal or state jurisdiction in particular, to a significant extent.

Usage

from datasets import load_dataset
dataset = load_dataset('lexlms/lex_files', name='eu-legislation')

Dataset Specifications

The LexFiles

Corpus	Corpus alias	Documents	Tokens	Pct.	Sampl. (a=0.5)	Sampl. (a=0.2)
🇪🇺 EU Legislation	eu-legislation	93.7K	233.7M	1.2%	5.0%	8.0%
🇪🇺 EU Court Decisions	eu-court-cases	29.8K	178.5M	0.9%	4.3%	7.6%
🇪🇺 ECtHR Decisions	ecthr-cases	12.5K	78.5M	0.4%	2.9%	6.5%
🇬🇧 UK Legislation	uk-legislation	52.5K	143.6M	0.7%	3.9%	7.3%
🇬🇧 UK Court Decisions	uk-court-cases	47K	368.4M	1.9%	6.2%	8.8%
🇮🇳 Indian Court Decisions	indian-court-cases	34.8K	111.6M	0.6%	3.4%	6.9%
🇨🇦 Canadian Legislation	canada-legislation	6K	33.5M	0.2%	1.9%	5.5%
🇨🇦 Canadian Court Decisions	canadian_decisions	11.3K	33.1M	0.2%	1.8%	5.4%
🇺🇸 U.S. Court Decisions [1]	us-court-cases	4.6M	11.4B	59.2%	34.7%	17.5%
🇺🇸 U.S. Legislation	us-legislation	518	1.4B	7.4%	12.3%	11.5%
🇺🇸 U.S. Contracts	us-contracts	622K	5.3B	27.3%	23.6%	15.0%
Total	lexlms/lex_files	5.8M	18.8B	100%	100%	100%

[1] We consider only U.S. Court Decisions from 1965 onwards (cf. post Civil Rights Act), as a hard threshold for cases relying on severely out-dated and in many cases harmful law standards. The rest of the corpora include more recent documents.

[2] Sampling (Sampl.) ratios are computed following the exponential sampling introduced by Lample et al. (2019).

Additional corpora not considered for pre-training, since they do not represent factual legal knowledge.

Corpus	Corpus alias	Documents	Tokens
🗺️ Legal web pages from C4	legal-c4	284K	340M

LegalLAMA: Legal Language Model Analysis

LegalLAMA is a diverse probing benchmark suite comprising 8 sub-tasks that aims to assess the acquaintance of legal knowledge that PLMs acquired in pre-training.

Usage

from datasets import load_dataset
dataset = load_dataset('lexlms/legal_lama', name='contract_sections')

Dataset Specifications

Corpus	Corpus alias	Examples	Avg. Tokens	Labels
🇨🇦 Criminal Code Sections (Canada)	canadian_sections	321	72	144
🇪🇺 Legal Terminology (EU)	cjeu_term	2,127	164	23
🇺🇸 Contractual Section Titles (US)	contract_sections	1,527	85	20
🇺🇸 Contract Types (US)	contract_types	1,089	150	15
🇪🇺 ECHR Articles (CoE)	ecthr_articles	5,072	69	13
🇪🇺 Legal Terminology (CoE)	ecthr_terms	6,803	97	250
🇺🇸 Crime Charges (US)	us_crimes	4,518	118	59
🇺🇸 Legal Terminology (US)	us_terms	5,829	308	92

Evaluating PLMs

We considered the following PLMs in our experimentations;

• RoBERTa
• LegalBERT
• CaseLawBERT
• PoL-BERT
• LexLM

To evaluate these PLMs on LegalLAMA, run the following script:

sh scripts_lama/run_lama.sh

LexLMs - Pre-trained Language Models (PLMs)

We release 2 new legal-oriented PLMs, dubbed LexLMs, warm-started from the RoBERTa models, and further pre-trained on the "LeXFiles" corpuss for 1M additional steps.

Model Name	Alias	Layers	Hidden Units	Attention Heads	Parameters
Lex-LM (Base)	lexlms/legal-roberta-base	12	768	12	123.9M
Lex-LM (Large)	lexlms/legal-roberta-large	24	1024	16	354.0M

Usage

You can load any model with the standard HF AutoModel code.

from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("lexlms/legal-roberta-base")
tokenizer = AutoTokenizer.from_pretrained("lexlms/legal-roberta-base")

Train Language Models

Train Tokenizer

Initially, we train a custom BPE tokenizer:

python train_tokenizer.py

Train Language Model

Then, we train an LM from scratch with Masked Language Modelling (MLM):

sh scripts_mlm/train_lm.sh

Model type (architecture, size) and other training specifications can be modified in the script:

MODEL_PATH='lexlm-large'
MODEL_MAX_LENGTH=512
TOTAL_STEPS=1000000
BATCH_SIZE=512

Fine-tune Language Models

Fine-tune on LexGLUE tasks

For example to fine-tune a PLM on EURLEX, you have to run:

sh lex-glue/scripts/run_eurlex.sh

PLM and other training specifications can be modified in the script:

MODEL_PATH='lexlms/legal-roberta-large'