202 citations · 261 across the 18 of their papers we have counts for
18 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…
A Survey on Evaluation of Large Language Models
Yupeng Chang, Xu Wang, Jindong Wang +13
Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications. As LLMs continue to…
KEST: Kernel Distance Based Efficient Self-Training for Improving Controllable Text Generation
Yuxi Feng, Xiaoyuan Yi, Laks V. S. Lakshmanan +1
Self-training (ST) has come to fruition in language understanding tasks by producing pseudo labels, which reduces the labeling bottleneck of language model fine-tuning. Nevertheles…
Efficient Cross-Lingual Transfer for Chinese Stable Diffusion with Images as Pivots
Jinyi Hu, Xu Han, Xiaoyuan Yi +4
Diffusion models have made impressive progress in text-to-image synthesis. However, training such large-scale models (e.g. Stable Diffusion), from scratch requires high computation…
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…
Recurrence Boosts Diversity! Revisiting Recurrent Latent Variable in Transformer-Based Variational AutoEncoder for Diverse Text Generation
Jinyi Hu, Xiaoyuan Yi, Wenhao Li +2
Variational Auto-Encoder (VAE) has been widely adopted in text generation. Among many variants, recurrent VAE learns token-wise latent variables with each conditioned on the preced…
Evade the Trap of Mediocrity: Promoting Diversity and Novelty in Text Generation via Concentrating Attention
Wenhao Li, Xiaoyuan Yi, Jinyi Hu +2
Recently, powerful Transformer architectures have proven superior in generating high-quality sentences. Nevertheless, these models tend to produce dull high-frequency phrases, seve…
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…
Unified Detoxifying and Debiasing in Language Generation via Inference-time Adaptive Optimization
Zonghan Yang, Xiaoyuan Yi, Peng Li +2
Warning: this paper contains model outputs exhibiting offensiveness and biases. Recently pre-trained language models (PLMs) have prospered in various natural language generation (N…
Fuse It More Deeply! A Variational Transformer with Layer-Wise Latent Variable Inference for Text Generation
Jinyi Hu, Xiaoyuan Yi, Wenhao Li +2
The past several years have witnessed Variational Auto-Encoder's superiority in various text generation tasks. However, due to the sequential nature of the text, auto-regressive de…
Native Chinese Reader: A Dataset Towards Native-Level Chinese Machine Reading Comprehension
Shusheng Xu, Yichen Liu, Xiaoyu Yi +3
We present Native Chinese Reader (NCR), a new machine reading comprehension (MRC) dataset with particularly long articles in both modern and classical Chinese. NCR is collected fro…
CCPM: A Chinese Classical Poetry Matching Dataset
Wenhao Li, Fanchao Qi, Maosong Sun +2
Poetry is one of the most important art forms of human languages. Recently many studies have focused on incorporating some linguistic features of poetry, such as style and sentimen…
Neural Quality Estimation with Multiple Hypotheses for Grammatical Error Correction
Zhenghao Liu, Xiaoyuan Yi, Maosong Sun +2
Grammatical Error Correction (GEC) aims to correct writing errors and help language learners improve their writing skills. However, existing GEC models tend to produce spurious cor…
Robust Front-End for Multi-Channel ASR using Flow-Based Density Estimation
Hyeongju Kim, Hyeonseung Lee, Woo Hyun Kang +2
For multi-channel speech recognition, speech enhancement techniques such as denoising or dereverberation are conventionally applied as a front-end processor. Deep learning-based fr…
MixPoet: Diverse Poetry Generation via Learning Controllable Mixed Latent Space
Xiaoyuan Yi, Ruoyu Li, Cheng Yang +2
As an essential step towards computer creativity, automatic poetry generation has gained increasing attention these years. Though recent neural models make prominent progress in so…
Chinese Poetry Generation with a Salient-Clue Mechanism
Xiaoyuan Yi, Ruoyu Li, Maosong Sun
As a precious part of the human cultural heritage, Chinese poetry has influenced people for generations. Automatic poetry composition is a challenge for AI. In recent years, signif…
Chinese Poetry Generation with a Working Memory Model
Xiaoyuan Yi, Maosong Sun, Ruoyu Li +1
As an exquisite and concise literary form, poetry is a gem of human culture. Automatic poetry generation is an essential step towards computer creativity. In recent years, several…
Generating Chinese Classical Poems with RNN Encoder-Decoder
Xiaoyuan Yi, Ruoyu Li, Maosong Sun
We take the generation of Chinese classical poem lines as a sequence-to-sequence learning problem, and build a novel system based on the RNN Encoder-Decoder structure to generate q…