3 citations · 9 across the 8 of their papers we have counts for
4 papers · 1 filter
Gaps or Hallucinations? Gazing into Machine-Generated Legal Analysis for Fine-grained Text Evaluations
Abe Bohan Hou, William Jurayj, Nils Holzenberger +2
Large Language Models (LLMs) show promise as a writing aid for professionals performing legal analyses. However, LLMs can often hallucinate in this setting, in ways difficult to re…
CLERC: A Dataset for Legal Case Retrieval and Retrieval-Augmented Analysis Generation
Abe Bohan Hou, Orion Weller, Guanghui Qin +5
Legal professionals need to write analyses that rely on citations to relevant precedents, i.e., previous case decisions. Intelligent systems assisting legal professionals in writin…
Stumbling Blocks: Stress Testing the Robustness of Machine-Generated Text Detectors Under Attacks
Yichen Wang, Shangbin Feng, Abe Bohan Hou +5
The widespread use of large language models (LLMs) is increasing the demand for methods that detect machine-generated text to prevent misuse. The goal of our study is to stress tes…
k-SemStamp: A Clustering-Based Semantic Watermark for Detection of Machine-Generated Text
Abe Bohan Hou, Jingyu Zhang, Yichen Wang +2
Recent watermarked generation algorithms inject detectable signatures during language generation to facilitate post-hoc detection. While token-level watermarks are vulnerable to pa…