8 papers · 1 filter
Mitigating Factual Hallucination in Large Reasoning Models via Mixed-Mode Advantage Regularization
Kaishen Wang, Tong Zheng, Xuehao Cui +3
Large reasoning models (LRMs) improve language model capabilities by generating explicit thinking traces before final answers. In factuality-oriented question answering (QA), such…
Learning from Self-Debate: Preparing Reasoning Models for Multi-Agent Debate
Chenxi Liu, Yanshuo Chen, Ruibo Chen +3
The reasoning abilities of large language models (LLMs) have been substantially improved by reinforcement learning with verifiable rewards (RLVR). At test time, collaborative reaso…
LLMs Improving LLMs: Agentic Discovery for Test-Time Scaling
Tong Zheng, Haolin Liu, Chengsong Huang +10
Test-time scaling (TTS) has become an effective approach for improving large language model performance by allocating additional computation during inference. However, existing TTS…
Improving Text-to-Image Generation with Input-Side Inference-Time Scaling
Ruibo Chen, Jiacheng Pan, Heng Huang +1
Recent advances in text-to-image (T2I) generation have achieved impressive results, yet existing models often struggle with simple or underspecified prompts, leading to suboptimal…
Leave No TRACE: Black-box Detection of Copyrighted Dataset Usage in Large Language Models via Watermarking
Jingqi Zhang, Ruibo Chen, Yingqing Yang +3
Large Language Models (LLMs) are increasingly fine-tuned on smaller, domain-specific datasets to improve downstream performance. These datasets often contain proprietary or copyrig…
Improved Unbiased Watermark for Large Language Models
Ruibo Chen, Yihan Wu, Junfeng Guo +1
As artificial intelligence surpasses human capabilities in text generation, the necessity to authenticate the origins of AI-generated content has become paramount. Unbiased waterma…