13 citations · 35 across the 8 of their papers we have counts for
8 papers
From Instructions to Intrinsic Human Values -- A Survey of Alignment Goals for Big Models
Jing Yao, Xiaoyuan Yi, Xiting Wang +2
Big models, exemplified by Large Language Models (LLMs), are models typically pre-trained on massive data and comprised of enormous parameters, which not only obtain significantly…
Towards Explainable Collaborative Filtering with Taste Clusters Learning
Yuntao Du, Jianxun Lian, Jing Yao +5
Collaborative Filtering (CF) is a widely used and effective technique for recommender systems. In recent decades, there have been significant advancements in latent embedding-based…
Generating Multiple-Length Summaries via Reinforcement Learning for Unsupervised Sentence Summarization
Dongmin Hyun, Xiting Wang, Chanyoung Park +2
Sentence summarization shortens given texts while maintaining core contents of the texts. Unsupervised approaches have been studied to summarize texts without human-written summari…
DuNST: Dual Noisy Self Training for Semi-Supervised Controllable Text Generation
Yuxi Feng, Xiaoyuan Yi, Xiting Wang +2
Self-training (ST) has prospered again in language understanding by augmenting the fine-tuning of pre-trained language models when labeled data is insufficient. However, it remains…
Prototypical Fine-tuning: Towards Robust Performance Under Varying Data Sizes
Yiqiao Jin, Xiting Wang, Yaru Hao +2
In this paper, we move towards combining large parametric models with non-parametric prototypical networks. We propose prototypical fine-tuning, a novel prototypical framework for…
Self-explaining deep models with logic rule reasoning
Seungeon Lee, Xiting Wang, Sungwon Han +3
We present SELOR, a framework for integrating self-explaining capabilities into a given deep model to achieve both high prediction performance and human precision. By "human precis…
Towards Fine-Grained Reasoning for Fake News Detection
Yiqiao Jin, Xiting Wang, Ruichao Yang +4
The detection of fake news often requires sophisticated reasoning skills, such as logically combining information by considering word-level subtle clues. In this paper, we move tow…
Analysis of Q-learning with Adaptation and Momentum Restart for Gradient Descent
Bowen Weng, Huaqing Xiong, Yingbin Liang +1
Existing convergence analyses of Q-learning mostly focus on the vanilla stochastic gradient descent (SGD) type of updates. Despite the Adaptive Moment Estimation (Adam) has been co…