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cs.CL2026

One Frozen Simulator Is Not Enough: Simulator Collapse in Multi-Agent RL

Simon Yu, Nicholas Tomlin, Marwa Abdulhai +7

Multi-agent reinforcement learning for human-AI interaction typically relies on a single large language model to simulate user behavior. We show that this approach systematically f…

cs.CL2024

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…

cs.CL2024

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…

cs.CL2024

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…

cs.CL2024

SemStamp: A Semantic Watermark with Paraphrastic Robustness for Text Generation

Abe Bohan Hou, Jingyu Zhang, Tianxing He +7

Existing watermarking algorithms are vulnerable to paraphrase attacks because of their token-level design. To address this issue, we propose SemStamp, a robust sentence-level seman…