In-Context Learning

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 LearningNatural Language ProcessingPrompt Engineering

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

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

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.

FocusICLIn-Context Learningattention mechanisms

0 likes · 16 min read

Focused Large Language Models are Stable Many-Shot Learners

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

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 Learninganchor tokens

0 likes · 13 min read

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

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 researchChain-of-ThoughtEmergence

0 likes · 13 min read

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

Sohu Tech Products

Mar 29, 2023 · Mobile Development

Exploring ChatGPT for Flutter UI: Generating a Yellow Star with Animation

This article recounts a developer's hands‑on experiment using ChatGPT to generate Flutter code for drawing a yellow five‑pointed star, iteratively refining the design, adding explosion particle animation, and reflecting on the strengths and quirks of in‑context learning AI.

AIChatGPTFlutter

0 likes · 13 min read

Exploring ChatGPT for Flutter UI: Generating a Yellow Star with Animation

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.

Chain-of-ThoughtChatGPTIn-Context Learning

0 likes · 15 min read

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

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.

GPT-3In-Context LearningMachine Learning

0 likes · 15 min read

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

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

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

Architect

Feb 9, 2023 · Artificial Intelligence

Emergent Abilities of Large Language Models: Complex Reasoning, Knowledge Reasoning, and Out‑of‑Distribution Robustness

This article reviews recent research on the emergent abilities of large language models—such as chain‑of‑thought reasoning, knowledge retrieval without external sources, and robustness to distribution shifts—examining scaling laws, model size thresholds, and the open questions surrounding a potential paradigm shift from fine‑tuning to in‑context learning.

AI researchEmergent AbilitiesIn-Context Learning

0 likes · 23 min read

Emergent Abilities of Large Language Models: Complex Reasoning, Knowledge Reasoning, and Out‑of‑Distribution Robustness

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?

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

Xiaohongshu Tech REDtech

Jan 3, 2023 · Artificial Intelligence

Insights into ChatGPT: Capabilities, Limitations, and Implications for AI Research

During Xiaohongshu’s REDtech livestream, AI researchers examined ChatGPT’s rapid adoption, versatile task performance, and underlying large‑scale pre‑training with in‑context learning, while highlighting persistent hallucinations, weak reasoning, high costs, and limited search‑engine replacement potential, and emphasized the importance of RLHF‑driven human feedback for future multimodal AI research.

AI researchChatGPTIn-Context Learning

0 likes · 14 min read

Insights into ChatGPT: Capabilities, Limitations, and Implications for AI Research