Papers by Chengyu Huang
SpanProto: A Two-stage Span-based Prototypical Network for Few-shot Named Entity Recognition (2022.emnlp-main)
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| Challenge: | Existing methods for few-shot Named Entity Recognition ignore entity boundaries and are time-consuming . a seminal span-based prototypical network solves the problem using two stages: span extraction and mention classification. |
| Approach: | They propose a seminal span-based prototypical network that tackles few-shot NER . they transform sequential tags into a global boundary matrix and use prototypical learning . |
| Outcome: | The proposed model outperforms strong baselines over multiple benchmarks. |
Distilling Instruction-following Abilities of Large Language Models with Task-aware Curriculum Planning (2024.findings-emnlp)
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| Challenge: | Instruction tuning aims to align large language models (LLMs) with open-domain instructions and human-preferred responses. |
| Approach: | They propose a multi-round distillation framework that uses an oracle LLM to select instructions that are difficult for a student LLM. |
| Outcome: | The proposed framework outperforms large language models and user-tuned models on several widely recognized benchmarks and multiple student LLMs. |
Thinking with DistilQwen: A Tale of Four Distilled Reasoning and Reward Model Series (2025.emnlp-industry)
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| Challenge: | In the rapidly evolving landscape of large language models, the need for efficient reasoning models has become increasingly urgent. |
| Approach: | They extend the Qwen model family by introducing four model series specifically designed for industrial applications. |
| Outcome: | The proposed models outperform previous models in multiple benchmarks and provide scalable training and inference functionality on the Alibaba Cloud PAI platform. |
PAI-Diffusion: Constructing and Serving a Family of Open Chinese Diffusion Models for Text-to-image Synthesis on the Cloud (2024.acl-demos)
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| Challenge: | Existing diffusion models fail to address the challenges of generating high-quality images from textual descriptions due to its large vocabulary size and complex character relationships. |
| Approach: | They propose a framework that integrates Chinese diffusion models with Alibaba Cloud's Platform for AI and enables the generation of contextually relevant images. |
| Outcome: | The proposed framework integrates with Alibaba Cloud’s Platform for AI, providing accessible and scalable solutions. |
Sharing, Teaching and Aligning: Knowledgeable Transfer Learning for Cross-Lingual Machine Reading Comprehension (2023.findings-emnlp)
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| Challenge: | X-STA is a new approach for cross-lingual machine reading comprehension . the variation of answer span positions in different languages makes it difficult to transfer knowledge across languages. |
| Approach: | They propose a method that leverages an attentive teacher to subtly transfer the answer spans of the source language to the answer output space of the target. |
| Outcome: | The proposed method outperforms state-of-the-art approaches on three multi-lingual datasets. |
DAFNet: Dynamic Auxiliary Fusion for Sequential Model Editing in Large Language Models (2024.findings-acl)
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Taolin Zhang, Qizhou Chen, Dongyang Li, Chengyu Wang, Xiaofeng He, Longtao Huang, Hui Xue’, Jun Huang
| Challenge: | Large language models (LLMs) have shown impressive results, but still suffer from hallucination, i.e., the generation of false information. |
| Approach: | They propose a task of sequential model editing that aims to rectify mistakes continuously. |
| Outcome: | The proposed method significantly outperforms baselines in single-turn and sequential editing. |
Conversation Disentanglement with Bi-Level Contrastive Learning (2022.findings-emnlp)
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| Challenge: | Existing methods focus on pairwise utterance relations but pay inadequate attention to utterant-to-context relation modeling. |
| Approach: | They propose a general disentangle model based on bi-level contrastive learning that brings closer utterances in the same session while encouraging each utterrance to be near its clustered session prototypes in representation space. |
| Outcome: | The proposed model achieves state-of-the-art performance on both settings across public datasets. |
KEHRL: Learning Knowledge-Enhanced Language Representations with Hierarchical Reinforcement Learning (2024.lrec-main)
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| Challenge: | General pre-trained language models (PLMs) leverage relation triples from knowledge graphs (KGs) and integrate external data sources into language models via self-supervised learning. |
| Approach: | They propose to learn Knowledge-Enhanced language representations with Hierarchical Reinforcement Learning (KEHRL) to detect positions for knowledge injection and integrate external knowledge into the model to avoid injecting inaccurate or irrelevant knowledge. |
| Outcome: | The proposed model can detect essential positions in texts for knowledge injection and integrate external knowledge into the model to avoid injecting inaccurate or irrelevant knowledge. |
Revisiting and Advancing Chinese Natural Language Understanding with Accelerated Heterogeneous Knowledge Pre-training (2022.emnlp-industry)
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Taolin Zhang, Junwei Dong, Jianing Wang, Chengyu Wang, Ang Wang, Yinghui Liu, Jun Huang, Yong Li, Xiaofeng He
| Challenge: | Existing knowledge-enhanced pre-trained language models (KEPLMs) can capture internal knowledge, but can't understand external background knowledge. |
| Approach: | They propose to use Chinese knowledge-enhanced pre-trained language models to improve context-aware representations via learning from structured relations in knowledge bases. |
| Outcome: | Experiments show that Chinese knowledge-enhanced pre-trained language models outperform strong baselines over various benchmark NLP tasks and in different model sizes. |
Knowledgeable In-Context Tuning: Exploring and Exploiting Factual Knowledge for In-Context Learning (2024.findings-naacl)
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| Challenge: | Existing studies have explored multiple aspects that affect the performance of large language models (LLMs) such as input-output mapping, extensive data resources, and the ability to train on labeled examples. |
| Approach: | They propose a framework that injects knowledge into LLMs during continual self-supervised pre-training and judiciously selects examples with high knowledge relevance. |
| Outcome: | The proposed framework outperforms baseline models and improves by more than 13% and 7% on text classification and question-answering tasks. |
Knowledge-Empowered Representation Learning for Chinese Medical Reading Comprehension: Task, Model and Resources (2021.findings-acl)
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| Challenge: | MRC is a popular task in NLP, aiming to understand a passage and answer the relevant questions. |
| Approach: | They propose a multi-target machine learning task for the medical domain that predicts answers to medical questions and corresponding support sentences from medical information sources simultaneously. |
| Outcome: | The proposed model outperforms baselines by fusing context-aware and knowledge-awful token representations. |
Towards Unified Prompt Tuning for Few-shot Text Classification (2022.findings-emnlp)
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Jianing Wang, Chengyu Wang, Fuli Luo, Chuanqi Tan, Minghui Qiu, Fei Yang, Qiuhui Shi, Songfang Huang, Ming Gao
| Challenge: | Prompt-based fine-tuning has boosted performance of Pre-trained Language Models (PLMs) on few-shot text classification, but PLMs are unfamiliar with prompt-style expressions during pre-training, which limits the few- shot learning performance on downstream tasks. |
| Approach: | They propose a framework for prompt-based fine-tuning that captures prompting semantics from non-target NLP datasets and propose 'Prompt-Options-Verbalizer' for joint prompt learning across different NLP tasks. |
| Outcome: | Experiments show that the proposed framework outperforms state-of-the-art prompt-based fine-tuning frameworks on few-shot text classification tasks. |
Better LLM Reasoning via Dual-Play (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) have made remarkable progress through Reinforcement Learning with Verifiable Rewards (RLVR) however, external supervision remains a bottleneck for tasks and domains for which supervised data are scarce or non-existent. |
| Approach: | They propose a novel dual-play framework that adversarially trains two models initialized from the same base model. |
| Outcome: | The proposed framework improves the math reasoning performance of large language models. |
Enhancing Reasoning Abilities of Small LLMs with Cognitive Alignment (2025.emnlp-main)
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| Challenge: | Existing methods to distill chain-of-thought (CoT) results from large language reasoning models (LRMs) to small models are ineffective and require substantial amount of annotated data. |
| Approach: | They propose a Critique-Rethink-Verify system for training small language reasoning models that can be critiquized according to the cognitive capabilities of smaller models. |
| Outcome: | The proposed system outperforms other methods on challenging reasoning benchmarks. |
TRELM: Towards Robust and Efficient Pre-training for Knowledge-Enhanced Language Models (2024.lrec-main)
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| Challenge: | Existing methods for incorporating external knowledge into language models do not prioritize learning embeddings for entity-related tokens. |
| Approach: | They propose a framework for incorporating external knowledge into pre-training models that utilize entity-related tokens. |
| Outcome: | The proposed framework reduces pre-training time by 50% and outperforms other KEPLMs in knowledge probing tasks and multiple knowledge-aware language understanding tasks. |
Text Augmentation in a Multi-Task View (2021.eacl-main)
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| Challenge: | a multi-task view of data augmentation allows for a more robust performance than traditional augmentation. |
| Approach: | They propose a multi-task view of data augmentation where original and augmented samples are weighted substantively during training. |
| Outcome: | The proposed model improves on three benchmark text classification datasets. |
Meta Fine-Tuning Neural Language Models for Multi-Domain Text Mining (2020.emnlp-main)
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| Challenge: | Pre-trained neural language models improve learning for various NLP tasks by fine-tuning them on task-specific training sets. |
| Approach: | They propose a meta-learning procedure to fine-tune neural language models on task-specific training sets. |
| Outcome: | The proposed procedure solves a group of similar NLP tasks on a text mining dataset. |
Training Language Models to Generate Text with Citations via Fine-grained Rewards (2024.acl-long)
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| Challenge: | Recent Large Language Models (LLMs) are prone to hallucination and their outputs often contain incorrect or unverifiable claims. |
| Approach: | They propose a training framework using fine-grained rewards to teach LLMs to generate highly supportive and relevant citations while ensuring the correctness of their responses. |
| Outcome: | The proposed training framework outperforms existing methods on QA datasets and surpasses GPT-3.5-turbo on LLaMA-2-7B. |
TransPrompt: Towards an Automatic Transferable Prompting Framework for Few-shot Text Classification (2021.emnlp-main)
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| Challenge: | Recent studies show that prompts improve performance of large pre-trained language models for few-shot text classification. |
| Approach: | They propose a prompt-based framework for few-shot learning that captures cross-task transferable knowledge and uses two de-biasing techniques to make it more task-agnostic and unbiased . |
| Outcome: | The proposed framework outperforms strong baselines over multiple NLP tasks and datasets. |
VideoCLIP-XL: Advancing Long Description Understanding for Video CLIP Models (2024.emnlp-main)
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| Challenge: | Existing studies have shown that CLIP models with short summary texts cannot process extensive textual descriptions due to its text encoder's reliance on positional embeddings with length 77. |
| Approach: | They propose a Contrastive Language-Image Pre-training (CLIP) model which aims to unleash the long-description understanding capability of video CLIP models. |
| Outcome: | The proposed model can learn the distribution of feature space while expanding the long description capability. |
Building a Family of Data Augmentation Models for Low-cost LLM Fine-tuning on the Cloud (2025.coling-industry)
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| Challenge: | Existing models for learning large language models are expensive and difficult to build and fine-tune. |
| Approach: | They propose a family of data augmentation models to improve model fine-tuning efficiency . they leverage powerful LLMs to expand, refine and re-write instructions and responses . |
| Outcome: | The proposed models improve the efficiency of model fine-tuning by leveraging small datasets and quality assessment techniques. |
Lifelong Knowledge Editing for LLMs with Retrieval-Augmented Continuous Prompt Learning (2024.emnlp-main)
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| Challenge: | Existing methods to correct outdated or erroneous knowledge in large language models (LLMs) are slow and cumbersome, resulting in catastrophic knowledge forgetting and degradation of model performance. |
| Approach: | They propose a RetriEval-augmented ContInuous Prompt lEarning method that converts knowledge statements into short and informative continuous prompts, prefixed to the LLM’s input query embedding. |
| Outcome: | The proposed method improves the performance of large language models (LLMs) while maintaining the overall performance of the model. |
ARTIST: A Transformer-based Chinese Text-to-Image Synthesizer Digesting Linguistic and World Knowledge (2022.findings-emnlp)
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| Challenge: | Text-to-Image Synthesis (TIS) is a popular task to convert natural language texts into realistic images. |
| Approach: | They propose a transformer-based Chinese text-to-image synthesizer for high-resolution image generation that incorporates linguistic and relational knowledge facts into the model to ensure better performance without the usage of ultra-large models. |
| Outcome: | The proposed model outperforms existing models in Chinese with linguistic and relational knowledge facts. |
From Complex to Simple: Unraveling the Cognitive Tree for Reasoning with Small Language Models (2023.findings-emnlp)
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| Challenge: | Existing methods to solve complex logical reasoning problems are cumbersome for language models. |
| Approach: | They propose to use iterative methodology to construct a cognitive tree using language models . they propose to generate multiple responses by utilizing in-context examples . |
| Outcome: | The proposed model achieves a performance level comparable to that of GPT-3.5 . the proposed model contains fewer parameters than 5% of the model with 175B parameters . |
GSM-Noise: Exploring and Enhancing Large Language Models’ Reasoning under Noisy Inputs (2026.findings-acl)
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| Challenge: | Large language models struggle when dealing with complex, ill-formed, or noisy inputs . open-source models are less robust, while closed-source ones are more robust . |
| Approach: | They propose to use GSM-Noise to refine inputs before engaging in in-depth analysis to improve LLM robustness under noisy conditions. |
| Outcome: | The proposed model can achieve consistent performance gains under noisy conditions with prompt engineering, supervised finetuning, and reinforcement learning. |
On the Role of Long-tail Knowledge in Retrieval Augmented Large Language Models (2024.acl-short)
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Dongyang Li, Junbing Yan, Taolin Zhang, Chengyu Wang, Xiaofeng He, Longtao Huang, Hui Xue’, Jun Huang
| Challenge: | Existing RAG methods focus on improving the task performance, without fine-grained process of knowledge. |
| Approach: | They propose a method that detects long-tail knowledge in large language models by analyzing retrieved documents and enhancing queries indiscriminately with retrieved information. |
| Outcome: | The proposed method achieves over 4x speedup in average inference time and consistent performance improvement in downstream tasks compared to existing pipelines. |
EasyDistill: A Comprehensive Toolkit for Effective Knowledge Distillation of Large Language Models (2025.emnlp-demos)
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| Challenge: | In this paper, we present EasyDistill, a comprehensive toolkit designed for effective black-box and white-box knowledge distillation (KD) of large language models. |
| Approach: | They propose a toolkit for effective black-box and white-box knowledge distillation (KD) of large language models (LLMs). |
| Outcome: | The framework offers data synthesis, supervised fine-tuning, ranking optimization, and reinforcement learning techniques specifically tailored for KD scenarios. |
EasyNLP: A Comprehensive and Easy-to-use Toolkit for Natural Language Processing (2022.emnlp-demos)
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Chengyu Wang, Minghui Qiu, Taolin Zhang, Tingting Liu, Lei Li, Jianing Wang, Ming Wang, Jun Huang, Wei Lin
| Challenge: | Pre-Trained Models (PTMs) have reshaped the development of natural language processing (NLP) but it is not easy to obtain high-performing PTMs without a large amount of labeled training data and deploy them online with fast inference speed. |
| Approach: | They propose to make it easy to build NLP applications with knowledge-enhanced pre-training and knowledge distillation. |
| Outcome: | EasyNLP supports a comprehensive suite of NLP algorithms and features knowledge-enhanced pre-training, knowledge distillation and few-shot learning functionalities. |
Uncertainty-aware Parameter-Efficient Self-training for Semi-supervised Language Understanding (2023.findings-emnlp)
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| Challenge: | Existing methods for pre-trained language models rely on noisy data, which can be expensive if all parameters are updated. |
| Approach: | They propose a self-training framework that incorporates Monte Carlo dropouts into the model and judiciously selects reliable pseudo-labeled examples based on confidence and certainty. |
| Outcome: | The proposed framework improves performance and efficiency over multiple tasks over multiple datasets. |
Towards Adaptive Prefix Tuning for Parameter-Efficient Language Model Fine-tuning (2023.acl-short)
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| Challenge: | Parameter-efficient fine-tuning only optimizes a few task-specific parameters with frozen pre-trained model. |
| Approach: | They propose to optimize a prefix vector inserted into Transformer layers to optimize the prefix . they propose to use a gate mechanism to adjust the prefixed to each layer . |
| Outcome: | The proposed approach improves on the SuperGLUE and NER datasets. |
Rapid Diffusion: Building Domain-Specific Text-to-Image Synthesizers with Fast Inference Speed (2023.acl-industry)
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Bingyan Liu, Weifeng Lin, Zhongjie Duan, Chengyu Wang, Wu Ziheng, Zhang Zipeng, Kui Jia, Lianwen Jin, Cen Chen, Jun Huang
| Challenge: | Text-to-Image Synthesis (TIS) aims to generate images based on textual inputs . but, current diffusion-based models lack entity knowledge and low inference speed . |
| Approach: | They propose a framework for training and deploying latent diffusion models with rich entity knowledge injected and optimized networks. |
| Outcome: | The proposed framework improves image quality and inference speed and can be used in industrial applications. |
DiffChat: Learning to Chat with Text-to-Image Synthesis Models for Interactive Image Creation (2024.findings-acl)
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| Challenge: | a novel method to align Large Language Models to "chat" with prompt-as-input Text-to-Image Synthesis models is proposed . a user-specified instruction can be used to create a high quality image . |
| Approach: | They propose a method to align Large Language Models to "chat" with prompt-as-input Text-to-Image Synthesis models for interactive image creation. |
| Outcome: | The proposed method can exhibit superior performance than baseline models and strong competitors based on automatic and human evaluations. |
HER: Human-like Reasoning and Reinforcement Learning for LLM Role-playing (2026.findings-acl)
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Chengyu Du, Xintao Wang, Aili Chen, Weiyuan Li, Rui Xu, Junteng Liu, Zishan Huang, Rong Tian, Zijun Sun, Yuhao Li, Liheng Feng, Deming Ding, Pengyu Zhao, Yanghua Xiao
| Challenge: | Existing models for LLM role-playing lack high-quality datasets with explicit reasoning traces and reliable reward signals aligned with human preferences. |
| Approach: | They propose a unified framework for cognitive-level persona simulation that strictly distinguishes characters’ first-person thinking processes from LLMs’ third-person reasoning. |
| Outcome: | The proposed framework outperforms the Qwen3-32B baseline model and achieves a 30.26% and 14.97% performance on the minimax benchmarks. |
Meta-KD: A Meta Knowledge Distillation Framework for Language Model Compression across Domains (2021.acl-long)
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| Challenge: | Pre-trained language models have been successful in NLP tasks, but their large size and long inference time limit their deployment in real-time applications. |
| Approach: | They propose a meta-teacher model that captures transferable knowledge across domains and passes it to students. |
| Outcome: | The proposed model can distill large teacher models into small student models with guidance from the meta-teacher. |
Mock Worlds, Real Skills: Building Small Agentic Language Models with Synthetic Tasks, Simulated Environments, and Rubric-Based Rewards (2026.acl-long)
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| Challenge: | Existing agentic training data are narrow in task variety and easily solved . real-world APIs lack diversity and are unstable for large-scale reinforcement learning rollout processes. |
| Approach: | They propose a framework that synthesizes diverse tool-use training data and simulates complete environments. |
| Outcome: | The proposed framework synthesizes diverse tool-use training data and simulates complete environments. |
KECP: Knowledge Enhanced Contrastive Prompting for Few-shot Extractive Question Answering (2022.emnlp-main)
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| Challenge: | Extractive Question Answering (EQA) is one of the most essential tasks in Machine Reading Comprehension (MRC). |
| Approach: | They propose a framework that transforms extractive question answering into a non-autoregressive Masked Language Modeling (MLM) generation problem. |
| Outcome: | The proposed framework outperforms state-of-the-art approaches in few-shot learning scenarios by a large margin. |
Reasoning with OmniThought: A Large CoT Dataset with Verbosity and Cognitive Difficulty Annotations (2026.acl-long)
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| Challenge: | Existing resources often fail to provide extensive reasoning problems with coherent CoT processes distilled from multiple teacher models. |
| Approach: | They propose a large-scale dataset featuring 2 million CoT processes generated by multiple powerful LRMs. |
| Outcome: | The proposed dataset features 2 million CoT processes and is validated by multiple powerful LRMs. |
FashionKLIP: Enhancing E-Commerce Image-Text Retrieval with Fashion Multi-Modal Conceptual Knowledge Graph (2023.acl-industry)
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Xiaodan Wang, Chengyu Wang, Lei Li, Zhixu Li, Ben Chen, Linbo Jin, Jun Huang, Yanghua Xiao, Ming Gao
| Challenge: | Recent advances in visual-language pre-trained (VLP) models have greatly improved cross-modal retrieval performance . however, the fine-grained interactions between objects from different modalities are far from well-established . e-commerce domain lacks sufficient training data and fine-granular cross-modulal knowledge . |
| Approach: | They propose a visual-language pre-trained (VLP) image-text retrieval model that integrates cross-modal knowledge into the model to improve performance. |
| Outcome: | The proposed model improves performance on e-commerce image-text retrieval task by a large margin. |
CocaCLIP: Exploring Distillation of Fully-Connected Knowledge Interaction Graph for Lightweight Text-Image Retrieval (2023.acl-industry)
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| Challenge: | Existing methods for text-image retrieval are limited to edge devices and real-time situations due to the substantial indexing and inference time. |
| Approach: | They propose a fully-Connected knowledge interaction graph technique for cross-modal pre-training distillation. |
| Outcome: | The proposed method achieves SOTA performances on the widely-used Flickr30K and MSCOCO benchmarks under the lightweight setting. |
XtremeCLIP: Extremely Parameter-efficient Tuning for Low-resource Vision Language Understanding (2023.findings-acl)
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| Challenge: | Existing approaches to fine-tune visual-language understanding (VLU) require tasks-specific designs and sufficient training data. |
| Approach: | They propose a simple yet efficient paradigm for low-resource Visual Language Understanding (VLU) they reformulate a series of VLU tasks as an open-book affinity-matching problem. |
| Outcome: | The proposed framework outperforms baselines in low-resource settings. |
DistilQwen2.5: Industrial Practices of Training Distilled Open Lightweight Language Models (2025.acl-industry)
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| Challenge: | Existing studies on distilled lightweight LLMs have focused on transferring knowledge from a larger model (the teacher) to a smaller model (sector). |
| Approach: | They propose a family of distilled, lightweight LLMs derived from Qwen2.5 models. |
| Outcome: | Experimental results show that the distilled models have significantly stronger instruction-following capabilities than the original models. |
Meta Distant Transfer Learning for Pre-trained Language Models (2021.emnlp-main)
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| Challenge: | Notable PLMs are available for text classification tasks, but performance of PLM on downstream tasks may be limited by the availability of training set. |
| Approach: | They propose a meta-learning framework to learn the transferable knowledge across tasks using PLMs. |
| Outcome: | The proposed framework outperforms baselines on seven datasets and is task-agnostic and unbiased. |
Few-Shot Text Classification with Triplet Networks, Data Augmentation, and Curriculum Learning (2021.naacl-main)
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| Challenge: | a few-shot text classification task requires a large number of output classes, with few training examples per class. |
| Approach: | They propose a data augmentation technique suitable for training with limited data for few-shot, highly-multiclass text classification scenarios. |
| Outcome: | The proposed technique improves performance on four classification tasks by 3.0% on average. |
DCRM: A Heuristic to Measure Response Pair Quality in Preference Optimization (2025.findings-emnlp)
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| Challenge: | Recent research has attempted to associate preference optimization (PO) performance with the underlying preference datasets. |
| Approach: | They propose a best-of-N2 pairing method that selects response pairs with the highest DCRM to measure the quality of a response pair for PO. |
| Outcome: | The proposed method produces training datasets that can further improve models’ performance on AlpacaEval, MT-Bench, and Arena-Hard over the existing training sets. |
AgenticQwen: Training Small Agentic Language Models with Dual Data Flywheels for Industrial-Scale Tool Use (2026.acl-industry)
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| Challenge: | Modern industrial applications increasingly demand language models capable of multi-step reasoning and tool use in real-world settings. |
| Approach: | They propose a model family that trains via multi-round reinforcement learning on synthetic data and open-source data. |
| Outcome: | The proposed model train on synthetic and open-source data achieves strong performance on multiple agentic benchmarks and in an industrial agent system. |
UniPSDA: Unsupervised Pseudo Semantic Data Augmentation for Zero-Shot Cross-Lingual Natural Language Understanding (2024.lrec-main)
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| Challenge: | Existing studies rely on shallow unsupervised data generated by token surface matching regardless of global context-aware semantics of the surrounding text tokens. |
| Approach: | They propose an Unsupervised Pseudo Semantic Data Augmentation mechanism to enrich training data without human intervention. |
| Outcome: | The proposed model improves on general zero-shot cross-lingual understanding tasks on different languages without human intervention. |
BeautifulPrompt: Towards Automatic Prompt Engineering for Text-to-Image Synthesis (2023.emnlp-industry)
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| Challenge: | Recent text-to-image models require multiple passes of prompt engineering by humans to produce satisfactory results for real-world applications. |
| Approach: | They propose a deep generative model to generate high-quality prompts from raw descriptions using visual feedback. |
| Outcome: | The proposed model produces high-quality prompts from simple raw descriptions . it can be integrated to a cloud-native AI platform to provide better image generation service in the cloud. |