4 citations · 6 across the 5 of their papers we have counts for
5 papers
Rewarding Better Thinking for LLM Preference Alignment
Xubo Liu, Wenya Guo, Ruxue Yan +2
LLM preference alignment aims to optimize models toward human preferences across diverse user instructions. Reinforcement learning has become a major post-training approach for thi…
From "What" to "How": Constrained Reasoning for Autoregressive Image Generation
Ruxue Yan, Xubo Liu, Wenya Guo +3
Autoregressive image generation has seen recent improvements with the introduction of chain-of-thought and reinforcement learning. However, current methods merely specify "What" de…
ProDS: Preference-oriented Data Selection for Instruction Tuning
Wenya Guo, Zhengkun Zhang, Xumeng Liu +5
Instruction data selection aims to identify a high-quality subset from the training set that matches or exceeds the performance of the full dataset on target tasks. Existing method…
AoM: Detecting Aspect-oriented Information for Multimodal Aspect-Based Sentiment Analysis
Ru Zhou, Wenya Guo, Xumeng Liu +3
Multimodal aspect-based sentiment analysis (MABSA) aims to extract aspects from text-image pairs and recognize their sentiments. Existing methods make great efforts to align the wh…
HyperPELT: Unified Parameter-Efficient Language Model Tuning for Both Language and Vision-and-Language Tasks
Zhengkun Zhang, Wenya Guo, Xiaojun Meng +5
The workflow of pretraining and fine-tuning has emerged as a popular paradigm for solving various NLP and V&L (Vision-and-Language) downstream tasks. With the capacity of pretraine…