5 papers
Beyond Scale and Generation: Understanding Language Model-based Entity Matching
Zeyu Zhang, Xue Li, Iacer Calixto +2
Entity matching identifies records that refer to the same real-world entity. Language models can be adapted to this task through bi-encoder, cross-encoder, and generative matcher a…
Seeing Is No Longer Believing: Frontier Image Generation Models, Synthetic Visual Evidence, and Real-World Risk
Shuai Wu, Xue Li, Yanna Feng +3
Frontier image generation has moved from artistic synthesis toward synthetic visual evidence. Systems such as GPT Image 2, Nano Banana Pro, Nano Banana 2, Nano Banana 2 Lite, Grok…
The Rise of Verbal Tics in Large Language Models: A Systematic Analysis Across Frontier Models
Shuai Wu, Xue Li, Yanna Feng +3
As Large Language Models (LLMs) continue to evolve through alignment techniques such as Reinforcement Learning from Human Feedback (RLHF) and Constitutional AI, a growing and incre…
Council Mode: A Heterogeneous Multi-Agent Consensus Framework for Reducing LLM Hallucination and Bias
Shuai Wu, Xue Li, Yanna Feng +3
Large Language Models (LLMs) have demonstrated advanced capabilities but often suffer from factual inaccuracies (hallucinations) and systematic biases. These issues, sometimes ampl…
Robust Test-time Video-Text Retrieval: Benchmarking and Adapting for Query Shifts
Bingqing Zhang, Zhuo Cao, Heming Du +4
Modern video-text retrieval (VTR) models excel on in-distribution benchmarks but are highly vulnerable to real-world query shifts, where the distribution of query data deviates fro…