5 papers
Modeling the Arrival Time of Information-Bearing Events in Natural Language
Leyi Lao, Yulin Ou, Yang Xu +1
Understanding how information is distributed over time is fundamental to modeling natural language. While existing empirical studies primarily rely on post-hoc analysis of informat…
Purging the Gray Zone: Latent-Geometric Denoising for Precise Knowledge Boundary Awareness
Hao An, Yibin Lou, Jiayi Guo +1
Large language models (LLMs) often exhibit hallucinations due to their inability to accurately perceive their own knowledge boundaries. Existing abstention fine-tuning methods typi…
Breaking the Safety-Capability Tradeoff: Reinforcement Learning with Verifiable Rewards Maintains Safety Guardrails in LLMs
Dongkyu Derek Cho, Huan Song, Arijit Ghosh Chowdhury +6
Fine-tuning large language models (LLMs) for downstream tasks typically exhibit a fundamental safety-capability tradeoff, where improving task performance degrades safety alignment…
Teaching LLMs to Abstain via Fine-Grained Semantic Confidence Reward
Hao An, Yang Xu
Mitigating hallucinations in Large Language Models (LLMs) is critical for their reliable deployment. Existing methods typically fine-tune LLMs to abstain from answering questions b…
Detecting Subtle Differences between Human and Model Languages Using Spectrum of Relative Likelihood
Yang Xu, Yu Wang, Hao An +2
Human and model-generated texts can be distinguished by examining the magnitude of likelihood in language. However, it is becoming increasingly difficult as language model's capabi…