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pretrained language models

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DataFunTalk
DataFunTalk
Dec 1, 2022 · Artificial Intelligence

Advances and Challenges in Controllable Text Generation with Pretrained Language Models

This report reviews the background, recent research progress, practical applications, and future directions of controllable text generation using transformer‑based pretrained language models, highlighting methods such as decoding strategies, prompt learning, memory networks, continual learning, contrastive training, and knowledge integration.

continual learningcontrastive trainingcontrollable text generation
0 likes · 13 min read
Advances and Challenges in Controllable Text Generation with Pretrained Language Models
DataFunTalk
DataFunTalk
Oct 10, 2022 · Artificial Intelligence

Model Compression and Deployment of Pre‑trained Language Models at Meituan

This article presents Meituan's practical experience with compressing large pre‑trained language models—covering challenges of large‑model deployment, compression techniques such as knowledge distillation, pruning and quantization, the AutoDisc assistant‑model approach, multi‑teacher and iterative distillation, and real‑world applications in search advertising, intelligent assistants, and dual‑tower semantic matching.

AI applicationsMeituanNLP
0 likes · 17 min read
Model Compression and Deployment of Pre‑trained Language Models at Meituan
DataFunSummit
DataFunSummit
Feb 12, 2022 · Artificial Intelligence

Advances and Challenges in Post‑BERT Semantic Matching: Negative Sampling, Data Augmentation, and Applications

After the BERT era, this article reviews the limitations of pre‑trained language models for semantic matching, discusses negative‑sample sampling, data‑augmentation techniques, contrastive learning methods such as ConSERT and SimCSE, and practical deployment considerations in vector‑based retrieval systems.

Vector Retrievalcontrastive learningdata augmentation
0 likes · 20 min read
Advances and Challenges in Post‑BERT Semantic Matching: Negative Sampling, Data Augmentation, and Applications
DataFunSummit
DataFunSummit
Mar 30, 2021 · Artificial Intelligence

Chinese Short‑Text Entity Linking: Model Design, Multitask Learning, and Experimental Results on the Qianyan Dataset

This article presents a comprehensive approach to Chinese short‑text entity linking, describing the Qianyan dataset, pipeline and end‑to‑end task formulations, sample construction, a multitask model that jointly performs entity ranking and NIL classification, various optimization techniques including confidence learning and adversarial training, and detailed experimental analysis showing state‑of‑the‑art performance.

Chinese NLPadversarial trainingconfidence learning
0 likes · 13 min read
Chinese Short‑Text Entity Linking: Model Design, Multitask Learning, and Experimental Results on the Qianyan Dataset
JD Tech
JD Tech
Feb 2, 2021 · Artificial Intelligence

Advances and Trends in Multimodal Digital Content Generation and Automatic Text Summarization

The article reviews recent research on multimodal digital content generation and automatic text summarization, outlining the evolution from extractive to abstractive methods, highlighting four key technology trends such as pretrained language models, transformer dominance, knowledge‑enhanced generation, and multimodal‑knowledge joint modeling, and describing an industrial e‑commerce application built on these advances.

Generative ModelsText Summarizatione-commerce
0 likes · 12 min read
Advances and Trends in Multimodal Digital Content Generation and Automatic Text Summarization
DataFunTalk
DataFunTalk
Dec 14, 2020 · Artificial Intelligence

Query Expansion Techniques: Relevance Modeling vs. Generative Approaches and Future Directions

This article reviews current query expansion methods, contrasting relevance‑based models that rely on terms or entities with generative models that encode whole queries, discusses challenges of handling long and complex queries, and surveys recent research on encoding queries, session modeling, and multi‑task feature integration.

Generative ModelsNLPQuery Expansion
0 likes · 9 min read
Query Expansion Techniques: Relevance Modeling vs. Generative Approaches and Future Directions