Search Results for author: Zehua Li

Found 3 papers, 1 papers with code

LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models

1 code implementation • NeurIPS 2023 • Neel Guha, Julian Nyarko, Daniel E. Ho, Christopher Ré, Adam Chilton, Aditya Narayana, Alex Chohlas-Wood, Austin Peters, Brandon Waldon, Daniel N. Rockmore, Diego Zambrano, Dmitry Talisman, Enam Hoque, Faiz Surani, Frank Fagan, Galit Sarfaty, Gregory M. Dickinson, Haggai Porat, Jason Hegland, Jessica Wu, Joe Nudell, Joel Niklaus, John Nay, Jonathan H. Choi, Kevin Tobia, Margaret Hagan, Megan Ma, Michael Livermore, Nikon Rasumov-Rahe, Nils Holzenberger, Noam Kolt, Peter Henderson, Sean Rehaag, Sharad Goel, Shang Gao, Spencer Williams, Sunny Gandhi, Tom Zur, Varun Iyer, Zehua Li

The advent of large language models (LLMs) and their adoption by the legal community has given rise to the question: what types of legal reasoning can LLMs perform?

Legal Reasoning

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Matching Tweets With Applicable Fact-Checks Across Languages

no code implementations • 14 Feb 2022 • Ashkan Kazemi, Zehua Li, Verónica Pérez-Rosas, Scott A. Hale, Rada Mihalcea

We conduct both classification and retrieval experiments, in monolingual (English only), multilingual (Spanish, Portuguese), and cross-lingual (Hindi-English) settings using multilingual transformer models such as XLM-RoBERTa and multilingual embeddings such as LaBSE and SBERT.

Fact Checking Retrieval

Paper
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Extractive and Abstractive Explanations for Fact-Checking and Evaluation of News

no code implementations • NAACL (NLP4IF) 2021 • Ashkan Kazemi, Zehua Li, Verónica Pérez-Rosas, Rada Mihalcea

In this paper, we explore the construction of natural language explanations for news claims, with the goal of assisting fact-checking and news evaluation applications.

Fact Checking Language Modelling +1

Paper
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