5 papers
PromptEmbedder: Efficient and Transferable Text Embedding via Dual-LLM Soft Prompting
Yu-Che Tsai, Kuan-Yu Chen, Yuan-Hao Chen +4
Large Language Models (LLMs) have demonstrated remarkable efficacy in text embedding, yet current adaptation methods like LoRA face significant bottlenecks in computational efficie…
On the Effect of Sampling Diversity in Scaling LLM Inference
Tianchun Wang, Zichuan Liu, Yuanzhou Chen +5
Large language model (LLM) scaling inference is key to unlocking greater performance, and leveraging diversity has proven an effective way to enhance it. Motivated by the observed…
Human Texts Are Outliers: Detecting LLM-generated Texts via Out-of-distribution Detection
Cong Zeng, Shengkun Tang, Yuanzhou Chen +6
The rapid advancement of large language models (LLMs) such as ChatGPT, DeepSeek, and Claude has significantly increased the presence of AI-generated text in digital communication.…
Humanizing the Machine: Proxy Attacks to Mislead LLM Detectors
Tianchun Wang, Yuanzhou Chen, Zichuan Liu +4
The advent of large language models (LLMs) has revolutionized the field of text generation, producing outputs that closely mimic human-like writing. Although academic and industria…
DALD: Improving Logits-based Detector without Logits from Black-box LLMs
Cong Zeng, Shengkun Tang, Xianjun Yang +7
The advent of Large Language Models (LLMs) has revolutionized text generation, producing outputs that closely mimic human writing. This blurring of lines between machine- and human…