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Found 5 papers, 5 papers with code
Gaps or Hallucinations? Gazing into Machine-Generated Legal Analysis for Fine-grained Text Evaluations
Large Language Models (LLMs) show promise as a writing aid for professionals performing legal analyses.
CLERC: A Dataset for Legal Case Retrieval and Retrieval-Augmented Analysis Generation
This dataset CLERC (Case Law Evaluation Retrieval Corpus), is constructed for training and evaluating models on their ability to (1) find corresponding citations for a given piece of legal analysis and to (2) compile the text of these citations (as well as previous context) into a cogent analysis that supports a reasoning goal.
Stumbling Blocks: Stress Testing the Robustness of Machine-Generated Text Detectors Under Attacks
Our experiments reveal that almost none of the existing detectors remain robust under all the attacks, and all detectors exhibit different loopholes.
k-SemStamp: A Clustering-Based Semantic Watermark for Detection of Machine-Generated Text
Recent watermarked generation algorithms inject detectable signatures during language generation to facilitate post-hoc detection.
SemStamp: A Semantic Watermark with Paraphrastic Robustness for Text Generation
Existing watermarking algorithms are vulnerable to paraphrase attacks because of their token-level design.
