BestHub
Sign in
Tag

NLP

0 views collected around this technical thread.

Didi Tech
Didi Tech
Apr 24, 2025 · Artificial Intelligence

Algorithmic Foundations and Evolution of Natural Language Processing

The article surveys the Algorithmic Foundations of Engineering R&D series, tracing NLP’s evolution from rule‑based systems to today’s multimodal large‑model era, reviewing core machine‑learning and deep‑learning techniques, transformer breakthroughs, representation learning, optimization methods, and emerging research such as retrieval‑augmented generation and AI agents.

AINLPTransformer
0 likes · 43 min read
Algorithmic Foundations and Evolution of Natural Language Processing
DataFunTalk
DataFunTalk
Apr 9, 2025 · Artificial Intelligence

The Origin of Large Language Models: A Historical Investigation of ULMFiT and Early LLMs

This article examines the historical roots of large language models, highlighting Jeremy Howard’s ULMFiT as a pioneering work, its influence on GPT‑1, and subsequent debates about which model truly qualifies as the first true LLM, supported by citations and expert commentary.

AI historyGPT-1LLM
0 likes · 7 min read
The Origin of Large Language Models: A Historical Investigation of ULMFiT and Early LLMs
Code Mala Tang
Code Mala Tang
Mar 27, 2025 · Artificial Intelligence

How Do BPE, WordPiece, and SentencePiece Shape Modern NLP Tokenization?

This article explains the fundamentals, workflows, examples, and trade‑offs of three major subword tokenization algorithms—Byte Pair Encoding, WordPiece, and SentencePiece—helping practitioners choose the right method for their large language model pipelines.

BPENLPSentencePiece
0 likes · 12 min read
How Do BPE, WordPiece, and SentencePiece Shape Modern NLP Tokenization?
Baidu Tech Salon
Baidu Tech Salon
Mar 21, 2025 · Artificial Intelligence

Semantic Embedding with Large Language Models: A Comprehensive Survey

This survey reviews the evolution of semantic embedding—from Word2vec and GloVe to BERT, Sentence‑BERT, and recent contrastive methods—then examines how large language models improve embeddings via synthetic data generation and backbone architectures, detailing techniques such as contrastive prompting, in‑context learning, knowledge distillation, and discussing resource, privacy, and interpretability challenges.

In-Context LearningNLPcontrastive learning
0 likes · 27 min read
Semantic Embedding with Large Language Models: A Comprehensive Survey
Cognitive Technology Team
Cognitive Technology Team
Mar 10, 2025 · Artificial Intelligence

Understanding Transformers: From NLP Challenges to Architecture and Core Mechanisms

This article explains the evolution of natural language processing, the limitations of rule‑based, statistical, and recurrent neural network models, and then introduces the Transformer architecture—covering word and position embeddings, self‑attention, multi‑head attention, Add & Norm, feed‑forward layers, and encoder‑decoder design—to help beginners grasp why Transformers solve key NLP problems.

AINLPSelf‑Attention
0 likes · 15 min read
Understanding Transformers: From NLP Challenges to Architecture and Core Mechanisms
DataFunTalk
DataFunTalk
Mar 2, 2025 · Artificial Intelligence

Top 10 AI Research Papers of 2024: Summaries, Contributions, and Practical Uses

This article presents a curated selection of ten groundbreaking 2024 AI research papers, detailing each model’s abstract, key contributions, and practical application scenarios across computer vision, multimodal learning, NLP, and efficient inference, offering readers inspiration and actionable insights for real‑world projects.

2024 researchAIComputer Vision
0 likes · 18 min read
Top 10 AI Research Papers of 2024: Summaries, Contributions, and Practical Uses
Test Development Learning Exchange
Test Development Learning Exchange
Jan 22, 2025 · Artificial Intelligence

Comprehensive Guide to Python Data Science Libraries with Code Examples

This article presents a concise tutorial on essential Python data science libraries, covering data cleaning with Pandas, numerical analysis with NumPy and SciPy, visualization with Matplotlib and Seaborn, machine learning with scikit‑learn, NLP with NLTK and spaCy, time‑series modeling, image processing, database access, and parallel computing, each illustrated with ready‑to‑run code examples.

NLPParallel ComputingPython
0 likes · 7 min read
Comprehensive Guide to Python Data Science Libraries with Code Examples
Test Development Learning Exchange
Test Development Learning Exchange
Jan 17, 2025 · Artificial Intelligence

Essential Python Libraries for Data Processing, Visualization, and Machine Learning

This article introduces ten essential Python libraries—including SciPy, Matplotlib, Plotly, Scikit‑learn, TensorFlow, spaCy, BeautifulSoup, OpenPyXL, Feather/Parquet, and SQLAlchemy—detailing their primary uses for scientific computing, visualization, machine learning, deep learning, NLP, web scraping, Excel handling, efficient data storage, and ORM, with practical code examples.

Data ProcessingLibrariesNLP
0 likes · 8 min read
Essential Python Libraries for Data Processing, Visualization, and Machine Learning
Test Development Learning Exchange
Test Development Learning Exchange
Nov 27, 2024 · Artificial Intelligence

Basic Natural Language Processing: Text Preprocessing and TF‑IDF with Python

This tutorial introduces fundamental natural language processing techniques, covering text preprocessing steps such as tokenization and stop‑word removal, followed by TF‑IDF feature extraction, and provides complete Python code examples to practice these concepts on a sample dataset.

NLPPythonTF-IDF
0 likes · 5 min read
Basic Natural Language Processing: Text Preprocessing and TF‑IDF with Python
DaTaobao Tech
DaTaobao Tech
Nov 13, 2024 · Artificial Intelligence

Understanding Neural Networks and Transformers: Principles, Implementation, and Applications

The article surveys neural networks from basic neuron operations and loss functions through deep architectures to the Transformer model, detailing embeddings, positional encoding, self‑attention, multi‑head attention, residual links, and encoder‑decoder design, and includes PyTorch code examples for linear regression, translation, and fine‑tuning Hugging Face’s MiniRBT for text classification.

AINLPPyTorch
0 likes · 44 min read
Understanding Neural Networks and Transformers: Principles, Implementation, and Applications
Tencent Cloud Developer
Tencent Cloud Developer
Oct 30, 2024 · Artificial Intelligence

Comprehensive Survey of AIGC Research: Papers, Resources, and Technical Overview

This survey acts as a comprehensive portal that organizes AIGC research across seven domains—text, image, and audio generation, cross‑modal association, text‑guided image and audio synthesis, and supporting resources—detailing seminal models such as GPT, Diffusion, CLIP, DALL·E, Stable Diffusion, MusicLM, and key papers that shaped each field.

AIGCClipComputer Vision
0 likes · 19 min read
Comprehensive Survey of AIGC Research: Papers, Resources, and Technical Overview
DevOps
DevOps
Oct 8, 2024 · Artificial Intelligence

Top 20+ Retrieval‑Augmented Generation (RAG) Interview Questions and Answers

This article presents over twenty essential Retrieval‑Augmented Generation (RAG) interview questions with detailed answers, covering fundamentals, applications, architecture, training, limitations, ethical considerations, and integration, offering AI enthusiasts and job candidates a comprehensive guide to mastering RAG concepts.

AI interviewNLPRAG
0 likes · 15 min read
Top 20+ Retrieval‑Augmented Generation (RAG) Interview Questions and Answers
58 Tech
58 Tech
Sep 23, 2024 · Artificial Intelligence

Enhancing Commercial Search with Knowledge Graphs and Large‑Model Techniques

This article describes how a commercial search platform iteratively upgrades its system by structuring business knowledge into a knowledge graph, applying multi‑stage entity extraction (CRF, Electra‑CRF, GLM‑3, OCR), and leveraging large language models to improve relevance, user experience, and revenue.

AINLPcommercial search
0 likes · 14 min read
Enhancing Commercial Search with Knowledge Graphs and Large‑Model Techniques
Zhuanzhuan Tech
Zhuanzhuan Tech
Sep 19, 2024 · Artificial Intelligence

Multi-Task Learning for Category Prediction in Zhaozhuan Search Intent Understanding

This article introduces multi‑task learning, reviews industry category‑prediction methods, and details Zhaozhuan's practical application of MTL to improve e‑commerce search intent understanding through hierarchical category, brand, and model prediction using RoBERTa and contrastive learning.

Artificial IntelligenceCategory PredictionNLP
0 likes · 11 min read
Multi-Task Learning for Category Prediction in Zhaozhuan Search Intent Understanding
JD Tech Talk
JD Tech Talk
Aug 29, 2024 · Artificial Intelligence

Content Compliance Domain Overview and Technical Solutions for Price Governance

The article outlines the role of the content compliance domain in e‑commerce, detailing user‑facing issues, business responsibilities, challenges in detection and mitigation, and technical solutions such as comparable‑price models, large‑scale price prediction, and merchant outreach, while also offering personal growth advice for compliance engineers.

AIComputer VisionNLP
0 likes · 9 min read
Content Compliance Domain Overview and Technical Solutions for Price Governance
DataFunSummit
DataFunSummit
Aug 27, 2024 · Artificial Intelligence

Applying Large Models to Xiao AI Assistant: Intent Routing, Understanding, and Response Generation

This article presents a comprehensive technical overview of how large language models are integrated into Xiaomi's Xiao AI assistant, detailing the architecture for intent routing, domain‑specific intent understanding, function‑calling mechanisms, fine‑tuning strategies, performance gains, and future research directions.

AI AssistantFine-tuningFunction Calling
0 likes · 14 min read
Applying Large Models to Xiao AI Assistant: Intent Routing, Understanding, and Response Generation
JD Retail Technology
JD Retail Technology
Aug 16, 2024 · Artificial Intelligence

Interview with JD Retail AI Director Zhai Zhouwei on the Evolution and Future of E‑commerce Search Powered by Large Models

In this interview, JD Retail’s AI director Zhai Zhouwei outlines the four historical stages of e‑commerce search, explains how large‑model AI is reshaping user interaction, retrieval and content generation, discusses practical challenges and solutions, and shares his vision and advice for enterprises adopting these technologies.

AIJD.comLarge Models
0 likes · 9 min read
Interview with JD Retail AI Director Zhai Zhouwei on the Evolution and Future of E‑commerce Search Powered by Large Models
AntTech
AntTech
Aug 13, 2024 · Artificial Intelligence

Ant Group Contributions to ACL 2024: Summaries of 14 Accepted Papers Across NLP and AI

From August 11‑16, 2024 the ACL conference in Bangkok featured 14 Ant Group papers covering large‑scale information extraction, decomposed LLMs for semantic search, multimodal hallucination detection, long‑context attention mechanisms, concept‑reasoning datasets, knowledge‑graph alignment, and more, highlighting the group's breadth in natural language processing and AI research.

ACL2024Information ExtractionNLP
0 likes · 20 min read
Ant Group Contributions to ACL 2024: Summaries of 14 Accepted Papers Across NLP and AI
Kuaishou Tech
Kuaishou Tech
Jul 23, 2024 · Artificial Intelligence

Parrot: Enhancing Multi-Turn Instruction Following for Large Language Models

This paper introduces Parrot, a system that enhances large language models' (LLMs) multi-turn instruction following capabilities through context-aware preference optimization (CaPO) and synthetic data generation, achieving significant performance improvements with limited training data.

AI researchCaPOMulti-turn Dialogue
0 likes · 9 min read
Parrot: Enhancing Multi-Turn Instruction Following for Large Language Models