5 papers · 1 filter
Trust Your Guide Only When Certain: Uncertainty-Aware Sparse Alignment at Inference Time
Zeen Zhu, Zhuo Li, Weiyang Guo +4
A prominent paradigm in inference-time alignment employs lightweight supervisors to steer Large Language Models (LLMs). Through empirical analysis, we identify a structural mismatc…
Temper and Tilt Lead to SLOP: Reward Hacking Mitigation with Inference-Time Alignment
Ye Wang, Jing Liu, Toshiaki Koike-Akino
Inference-time alignment techniques offer a lightweight alternative or complement to costly reinforcement learning, while enabling continual adaptation as alignment objectives and…
Mitigating Context-Memory Conflicts in LLMs through Dynamic Cognitive Reconciliation Decoding
Yigeng Zhou, Wu Li, Yifan Lu +6
Large language models accumulate extensive parametric knowledge through pre-training. However, knowledge conflicts occur when outdated or incorrect parametric knowledge conflicts w…
Team-Based Self-Play With Dual Adaptive Weighting for Fine-Tuning LLMs
Wu Li, Yigeng Zhou, Zesheng Shi +3
While recent self-training approaches have reduced reliance on human-labeled data for aligning LLMs, they still face critical limitations: (i) sensitivity to synthetic data quality…
LocateEdit-Bench: A Benchmark for Instruction-Based Editing Localization
Shiyu Wu, Shuyan Li, Jing Li +2
Recent advancements in image editing have enabled highly controllable and semantically-aware alteration of visual content, posing unprecedented challenges to manipulation localizat…