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Found 6 papers, 5 papers with code
FLawN-T5: An Empirical Examination of Effective Instruction-Tuning Data Mixtures for Legal Reasoning
In this work, we curate LawInstruct, a large legal instruction dataset, covering 17 jurisdictions, 24 languages and a total of 12M examples.
NLP Systems That Can't Tell Use from Mention Censor Counterspeech, but Teaching the Distinction Helps
The use of words to convey speaker's intent is traditionally distinguished from the `mention' of words for quoting what someone said, or pointing out properties of a word.
Holistic Evaluation of Language Models
We present Holistic Evaluation of Language Models (HELM) to improve the transparency of language models.
Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset
One concern with the rise of large language models lies with their potential for significant harm, particularly from pretraining on biased, obscene, copyrighted, and private information.
On the Opportunities and Risks of Foundation Models
AI is undergoing a paradigm shift with the rise of models (e. g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks.
When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset
While a Transformer architecture (BERT) pretrained on a general corpus (Google Books and Wikipedia) improves performance, domain pretraining (using corpus of approximately 3. 5M decisions across all courts in the U. S. that is larger than BERT's) with a custom legal vocabulary exhibits the most substantial performance gains with CaseHOLD (gain of 7. 2% on F1, representing a 12% improvement on BERT) and consistent performance gains across two other legal tasks.
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