699 citations · 783 across the 13 of their papers we have counts for
13 papers
Introspective Diffusion Language Models
Yifan Yu, Yuqing Jian, Junxiong Wang +12
Diffusion language models promise parallel generation, yet still lag behind autoregressive (AR) models in quality. We stem this gap to a failure of introspective consistency: AR mo…
LIONs: An Empirically Optimized Approach to Align Language Models
Xiao Yu, Qingyang Wu, Yu Li +1
Alignment is a crucial step to enhance the instruction-following and conversational abilities of language models. Despite many recent work proposing new algorithms, datasets, and t…
Visual Instruction Tuning
Haotian Liu, Chunyuan Li, Qingyang Wu +1
Instruction tuning large language models (LLMs) using machine-generated instruction-following data has improved zero-shot capabilities on new tasks, but the idea is less explored i…
FaceChat: An Emotion-Aware Face-to-face Dialogue Framework
Deema Alnuhait, Qingyang Wu, Zhou Yu
While current dialogue systems like ChatGPT have made significant advancements in text-based interactions, they often overlook the potential of other modalities in enhancing the ov…
GLIGEN: Open-Set Grounded Text-to-Image Generation
Yuheng Li, Haotian Liu, Qingyang Wu +5
Large-scale text-to-image diffusion models have made amazing advances. However, the status quo is to use text input alone, which can impede controllability. In this work, we propos…
Memformer: A Memory-Augmented Transformer for Sequence Modeling
Qingyang Wu, Zhenzhong Lan, Kun Qian +3
Transformers have reached remarkable success in sequence modeling. However, these models have efficiency issues as they need to store all the history token-level representations as…
Perception Score, A Learned Metric for Open-ended Text Generation Evaluation
Jing Gu, Qingyang Wu, Zhou Yu
Automatic evaluation for open-ended natural language generation tasks remains a challenge. Existing metrics such as BLEU show a low correlation with human judgment. We propose a no…
On the Generation of Medical Dialogues for COVID-19
Wenmian Yang, Guangtao Zeng, Bowen Tan +9
Under the pandemic of COVID-19, people experiencing COVID19-related symptoms or exposed to risk factors have a pressing need to consult doctors. Due to hospital closure, a lot of c…
A Tailored Pre-Training Model for Task-Oriented Dialog Generation
Jing Gu, Qingyang Wu, Chongruo Wu +2
The recent success of large pre-trained language models such as BERT and GPT-2 has suggested the effectiveness of incorporating language priors in downstream dialog generation task…
TextGAIL: Generative Adversarial Imitation Learning for Text Generation
Qingyang Wu, Lei Li, Zhou Yu
Generative Adversarial Networks (GANs) for text generation have recently received many criticisms, as they perform worse than their MLE counterparts. We suspect previous text GANs'…
Importance-Aware Learning for Neural Headline Editing
Qingyang Wu, Lei Li, Hao Zhou +2
Many social media news writers are not professionally trained. Therefore, social media platforms have to hire professional editors to adjust amateur headlines to attract more reade…
Alternating Recurrent Dialog Model with Large-scale Pre-trained Language Models
Qingyang Wu, Yichi Zhang, Yu Li +1
Existing dialog system models require extensive human annotations and are difficult to generalize to different tasks. The recent success of large pre-trained language models such a…
Quantifying Intrinsic Uncertainty in Classification via Deep Dirichlet Mixture Networks
Qingyang Wu, He Li, Lexin Li +1
With the widespread success of deep neural networks in science and technology, it is becoming increasingly important to quantify the uncertainty of the predictions produced by deep…