7 papers
Joint Alignment and Distillation for Video Generation via Sample-Guided Distribution Matching
Jiuzhou Lin, Junlong Wu, Fei Zuo +11
Aligning video generative models to human preferences heavily relies on Reinforcement Learning (RL), which suffers from extensive computational overhead. Existing workflows typical…
Step Back to Move Forward: Reflection-Aware Preference Optimization for Visual Generation
Junlong Wu, Jiuzhou Lin, Jia Sun +7
Diffusion models have become the mainstream paradigm for modern visual generation and have substantially advanced multimedia content synthesis, especially in text-to-image and text…
MASTE: A Multi-Agent Pipeline for Zero-Shot Aspect Sentiment Triplet Extraction
Ao Hong, Lehang Wang, Zhirun Yue +3
Aspect Sentiment Triplet Extraction (ASTE) requires jointly identifying (aspect, opinion, sentiment) triples from a given review sentence. While large language models (LLMs) achiev…
ReflectRM: Boosting Generative Reward Models via Self-Reflection within a Unified Judgment Framework
Kai Qin, Liangxin Liu, Yu Liang +7
Reward Models (RMs) are critical components in the Reinforcement Learning from Human Feedback (RLHF) pipeline, directly determining the alignment quality of Large Language Models (…
Let the Agent Search: Autonomous Exploration Beats Rigid Workflows in Temporal Question Answering
Xufei Lv, Jiahui Yang, Haoyuan Sun +5
Temporal Knowledge Graph Question Answering (TKGQA) is challenging because it requires multi-hop reasoning under complex temporal constraints. Recent LLM-based approaches have impr…
Distribution Preference Optimization: A Fine-grained Perspective for LLM Unlearning
Kai Qin, Jiaqi Wu, Jianxiang He +8
As Large Language Models (LLMs) demonstrate remarkable capabilities learned from vast corpora, concerns regarding data privacy and safety are receiving increasing attention. LLM un…
The Hidden Link Between RLHF and Contrastive Learning
Xufei Lv, Kehai Chen, Haoyuan Sun +3
Alignment of large language models (LLMs) with human values has recently garnered significant attention, with prominent examples including the canonical yet costly Reinforcement Le…