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Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Mar 14, 2026 · Artificial Intelligence

Can Large Language Models Get Stronger Without Human Language Training? A New Pre‑Pre‑Training Path

A recent study shows that pre‑training Transformers on synthetic, non‑language data generated by Neural Cellular Automata can boost language‑model performance by up to 6%, accelerate convergence by 40%, and improve downstream reasoning, even outperforming models trained on massive natural‑text corpora.

In-Context LearningNeural Cellular AutomataPre‑training
0 likes · 12 min read
Can Large Language Models Get Stronger Without Human Language Training? A New Pre‑Pre‑Training Path
AI2ML AI to Machine Learning
AI2ML AI to Machine Learning
Oct 24, 2025 · Artificial Intelligence

Beyond RAG: Three Emerging Knowledge‑Engineering Strategies (ICL, Online Learning, SLM)

The article outlines three post‑RAG knowledge‑engineering approaches—In‑Context Learning with dynamic few‑shot selection, Online Learning encompassing Meta‑Learning and Lifelong Learning to quickly adapt to new tasks, and the Small Language Model path that combines fine‑tuned task‑specific experts with LLM‑SLM collaboration for efficient, privacy‑preserving inference.

In-Context LearningKnowledge EngineeringLLM
0 likes · 4 min read
Beyond RAG: Three Emerging Knowledge‑Engineering Strategies (ICL, Online Learning, SLM)
Amap Tech
Amap Tech
Oct 17, 2025 · Artificial Intelligence

How Ranking Improves In-Context Example Retrieval: Insights from NeurIPS ’25

This article explains the limitations of current pointwise in‑context learning methods, introduces a novel ranking‑based approach called SeDPO that learns preference orders among examples, and demonstrates its superior performance across multiple NLP tasks through extensive experiments and ablation studies.

In-Context LearningNeurIPSSeDPO
0 likes · 10 min read
How Ranking Improves In-Context Example Retrieval: Insights from NeurIPS ’25
Cognitive Technology Team
Cognitive Technology Team
Mar 30, 2025 · Artificial Intelligence

Why Prompt Engineering Is the “Mind‑Reading” Technique of AI: The Crucial Role of In‑Context Learning

Prompt engineering uses in‑context learning to turn large language models into precise, task‑aware assistants by providing well‑crafted prompts that guide the model’s probability distribution, reduce hallucinations, and unlock hidden knowledge without any parameter tuning.

In-Context LearningPrompt engineeringartificial intelligence
0 likes · 6 min read
Why Prompt Engineering Is the “Mind‑Reading” Technique of AI: The Crucial Role of In‑Context Learning
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
Xiaohongshu Tech REDtech
Xiaohongshu Tech REDtech
Dec 26, 2024 · Artificial Intelligence

Focused Large Language Models are Stable Many-Shot Learners

FocusICL mitigates the reverse‑scaling of in‑context learning by masking irrelevant tokens and applying hierarchical batch attention, cutting attention complexity, and delivering consistent query focus that yields average accuracy gains of about 5 % across multiple LLMs and benchmarks.

Few‑Shot LearningFocusICLIn-Context Learning
0 likes · 16 min read
Focused Large Language Models are Stable Many-Shot Learners
DataFunSummit
DataFunSummit
Jul 29, 2024 · Artificial Intelligence

Large Language Models for Recommendation Systems: Current Progress, Challenges, and Future Directions

This article reviews the state‑of‑the‑art applications of large language models in recommendation systems, summarizing background knowledge, recent advances such as LLM4Rec, various tuning strategies, agent‑based approaches, open research problems, and future directions for generative recommendation.

AIIn-Context LearningLLM
0 likes · 24 min read
Large Language Models for Recommendation Systems: Current Progress, Challenges, and Future Directions
DataFunTalk
DataFunTalk
Dec 21, 2023 · Artificial Intelligence

Label Words are Anchors: An Information Flow Perspective for Understanding In-Context Learning – Best Long Paper at EMNLP 2023

At EMNLP 2023, the joint WeChat AI and Peking University paper 'Label Words are Anchors: An Information Flow Perspective for Understanding In-Context Learning' won the Best Long Paper award, revealing that label tokens act as anchors driving information aggregation in shallow layers and prediction flow in deep layers, and proposing methods to improve and diagnose in‑context learning.

AI researchIn-Context LearningInformation Flow
0 likes · 13 min read
Label Words are Anchors: An Information Flow Perspective for Understanding In-Context Learning – Best Long Paper at EMNLP 2023
Alibaba Cloud Developer
Alibaba Cloud Developer
Jul 12, 2023 · Artificial Intelligence

Can Large Language Models Transform Recommendation Systems?

This article reviews how recent large language models (LLMs) are reshaping recommendation systems, covering their emergence, in‑context learning, prompt‑based strategies, three main LLM‑driven recommendation paradigms, key research papers, experimental results, and future research directions.

In-Context LearningLLMPrompt engineering
0 likes · 20 min read
Can Large Language Models Transform Recommendation Systems?
Architect
Architect
Apr 19, 2023 · Artificial Intelligence

Emergence in Large Language Models: Phenomena, Explanations, and Implications

This article reviews the emergence phenomena observed in large language models, explains how model scale, in‑context learning and chain‑of‑thought prompting contribute to sudden performance gains, discusses small‑model alternatives, and explores the relationship between emergence and the training‑time Grokking effect.

AI researchEmergenceIn-Context Learning
0 likes · 13 min read
Emergence in Large Language Models: Phenomena, Explanations, and Implications
DataFunTalk
DataFunTalk
Feb 21, 2023 · Artificial Intelligence

Analysis of Large Language Models: Capabilities, Training Methods, and Limitations – Summary of Prof. Qiu Xipeng’s Lecture

Prof. Qiu Xipeng’s lecture provides a comprehensive overview of large language models—from their historical development and architectural foundations to key technologies such as in‑context learning, chain‑of‑thought, and natural‑instruction learning, as well as RLHF training, capability evaluation, and current limitations of ChatGPT.

ChatGPTIn-Context LearningModel Evaluation
0 likes · 15 min read
Analysis of Large Language Models: Capabilities, Training Methods, and Limitations – Summary of Prof. Qiu Xipeng’s Lecture
DataFunSummit
DataFunSummit
Feb 19, 2023 · Artificial Intelligence

Understanding In-Context Learning in Large Language Models: Experiments, Analysis, and Theoretical Insights

This article explains the concept of in‑context learning in large language models, presents experimental evaluations such as copy‑output, date‑formatting, and label‑remapping tasks, and discusses a recent theoretical analysis that links attention layers to implicit gradient‑based fine‑tuning, highlighting why model scale and data volume matter.

Attention MechanismFew‑Shot LearningGPT-3
0 likes · 15 min read
Understanding In-Context Learning in Large Language Models: Experiments, Analysis, and Theoretical Insights
Architect
Architect
Feb 18, 2023 · Artificial Intelligence

Paradigm Shifts in Large Language Models: From Pre‑training to AGI and Future Research Directions

The article reviews the evolution of large language models, highlighting two major paradigm shifts after GPT‑3, the role of scaling laws, knowledge acquisition, prompting techniques, reasoning abilities, and outlines future research priorities for building more capable and efficient AI systems.

AI reasoningIn-Context LearningModel Scaling
0 likes · 71 min read
Paradigm Shifts in Large Language Models: From Pre‑training to AGI and Future Research Directions
Xiaohongshu Tech REDtech
Xiaohongshu Tech REDtech
Feb 10, 2023 · Artificial Intelligence

Expert Insights on ChatGPT: Technical Challenges, Applications, and Future Directions

In a REDtech live interview, NLP professor Li Lei and Xiaohongshu engineers examined ChatGPT’s strengths—long, topic‑focused replies and few‑shot learning—and its challenges such as hallucinations, safety, lack of real‑time data, model compression, and multimodal AIGC, outlining how the technology could reshape content creation, customer service, and search while requiring careful risk management.

AIAI SafetyChatGPT
0 likes · 20 min read
Expert Insights on ChatGPT: Technical Challenges, Applications, and Future Directions
DataFunSummit
DataFunSummit
Feb 7, 2023 · Artificial Intelligence

How to Evaluate OpenAI's Super Conversational Model ChatGPT?

This article compiles three highly upvoted Zhihu answers that examine OpenAI's ChatGPT, discussing its breakthrough impact on NLP, visual in‑context learning, reinforcement‑learning‑from‑human‑feedback, and the broader implications for AI research and development.

AI researchChatGPTIn-Context Learning
0 likes · 10 min read
How to Evaluate OpenAI's Super Conversational Model ChatGPT?
NewBeeNLP
NewBeeNLP
Feb 7, 2023 · Artificial Intelligence

Mastering ChatGPT Prompt Engineering: Principles, Steps, and Real-World Examples

This article provides a comprehensive guide to ChatGPT prompt engineering, covering background concepts, design principles, step‑by‑step workflows, diverse use‑case examples, model limitations, and references to key research papers, helping readers craft effective prompts for various NLP tasks.

AIChatGPTIn-Context Learning
0 likes · 30 min read
Mastering ChatGPT Prompt Engineering: Principles, Steps, and Real-World Examples
DataFunTalk
DataFunTalk
Jan 10, 2023 · Artificial Intelligence

Paradigm Shifts in Large Language Model Research and Future Directions

The article reviews the evolution of large language models from the pre‑GPT‑3 era to the present, analyzes the conceptual and technical gaps between Chinese and global research, and outlines key future research directions such as scaling laws, prompting techniques, multimodal training, and efficient model architectures.

AI researchChatGPTIn-Context Learning
0 likes · 73 min read
Paradigm Shifts in Large Language Model Research and Future Directions