5 papers
Toward Beginner-Friendly LLMs for Language Learning: Controlling Difficulty in Conversation
Meiqing Jin, Liam Dugan, Chris Callison-Burch
Practicing conversations with large language models (LLMs) presents a promising alternative to traditional in-person language learning. However, most LLMs generate text at a near-n…
Machine Text Detectors are Membership Inference Attacks
Ryuto Koike, Liam Dugan, Masahiro Kaneko +2
Although membership inference attacks (MIAs) and machine-generated text detection target different goals, their methods often exploit similar signals based on a language model's pr…
Group-Adaptive Threshold Optimization for Robust AI-Generated Text Detection
Minseok Jung, Cynthia Fuertes Panizo, Liam Dugan +4
The advancement of large language models (LLMs) has made it difficult to differentiate human-written text from AI-generated text. Several AI-text detectors have been developed in r…
Domain Gating Ensemble Networks for AI-Generated Text Detection
Arihant Tripathi, Liam Dugan, Charis Gao +6
As state-of-the-art language models continue to improve, the need for robust detection of machine-generated text becomes increasingly critical. However, current state-of-the-art ma…
GenAI Content Detection Task 3: Cross-Domain Machine-Generated Text Detection Challenge
Liam Dugan, Andrew Zhu, Firoj Alam +3
Recently there have been many shared tasks targeting the detection of generated text from Large Language Models (LLMs). However, these shared tasks tend to focus either on cases wh…