2.3k citations · 2.5k across the 8 of their papers we have counts for
6 papers · 1 filter
NLP Systems That Can't Tell Use from Mention Censor Counterspeech, but Teaching the Distinction Helps
Kristina Gligoric, Myra Cheng, Lucia Zheng +2
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. Here we…
LawInstruct: A Resource for Studying Language Model Adaptation to the Legal Domain
Joel Niklaus, Lucia Zheng, Arya D. McCarthy +7
Instruction tuning is an important step in making language models useful for direct user interaction. However, the legal domain is underrepresented in typical instruction datasets…
When Benchmarks are Targets: Revealing the Sensitivity of Large Language Model Leaderboards
Norah Alzahrani, Hisham Abdullah Alyahya, Yazeed Alnumay +9
Large Language Model (LLM) leaderboards based on benchmark rankings are regularly used to guide practitioners in model selection. Often, the published leaderboard rankings are take…
Holistic Evaluation of Language Models
Percy Liang, Rishi Bommasani, Tony Lee +47
Language models (LMs) are becoming the foundation for almost all major language technologies, but their capabilities, limitations, and risks are not well understood. We present Hol…
Pile of Law: Learning Responsible Data Filtering from the Law and a 256GB Open-Source Legal Dataset
Peter Henderson, Mark S. Krass, Lucia Zheng +4
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 inform…
When Does Pretraining Help? Assessing Self-Supervised Learning for Law and the CaseHOLD Dataset
Lucia Zheng, Neel Guha, Brandon R. Anderson +2
While self-supervised learning has made rapid advances in natural language processing, it remains unclear when researchers should engage in resource-intensive domain-specific pretr…