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Machine Heart
Machine Heart
May 18, 2026 · Artificial Intelligence

Can Large Models Reason Deeply with Only a Few Thinking Tokens?

The paper introduces Heima, a framework that compresses chain‑of‑thought reasoning into a small set of abstract “thinking tokens” for multimodal large models, dramatically reducing generated tokens while preserving inference capability, and provides an adaptive interpreter to reconstruct human‑readable reasoning for analysis.

chain-of-thoughtefficient inferencelatent reasoning
0 likes · 12 min read
Can Large Models Reason Deeply with Only a Few Thinking Tokens?
Lao Guo's Learning Space
Lao Guo's Learning Space
May 12, 2026 · Artificial Intelligence

Demystifying the Core Technologies Behind ChatGPT, GPT‑4, and DeepSeek

This article breaks down the key algorithms that power large‑language models—Transformer, Mixture‑of‑Experts, Flash Attention, KV‑Cache, Multi‑Token Prediction, quantization, Chain‑of‑Thought and Retrieval‑Augmented Generation—explaining how each contributes to the performance of ChatGPT, GPT‑4 and DeepSeek.

Flash AttentionKV cacheMixture of Experts
0 likes · 10 min read
Demystifying the Core Technologies Behind ChatGPT, GPT‑4, and DeepSeek
DataFunTalk
DataFunTalk
May 10, 2026 · Artificial Intelligence

DeepSeek vs MCTS: Decoding the ‘Chicken & Liquor’ Dilemma in LLM Training

The article analyzes why DeepSeek’s large‑model training struggles with Monte‑Carlo Tree Search, explains its use of Chain‑of‑Thought prompting, GRPO entropy‑boosting and rejection‑sampling fine‑tuning, compares these methods with Google’s OmegaPRM and PRM approaches, and proposes a concrete MCTS‑driven data‑generation pipeline to overcome the “chicken and liquor” trade‑off.

DeepSeekGRPOMonte Carlo Tree Search
0 likes · 14 min read
DeepSeek vs MCTS: Decoding the ‘Chicken & Liquor’ Dilemma in LLM Training
DataFunSummit
DataFunSummit
May 4, 2026 · Artificial Intelligence

DeepSeek’s MCTS Failure: The ‘Roast Chicken and Baijiu’ Dilemma in LLM Training

The article examines why DeepSeek’s large‑model training cannot yet leverage Monte‑Carlo Tree Search, detailing its reliance on SFT, GRPO‑driven CoT activation and rejection‑sampling, contrasting this with Google’s PRM‑based approaches, and proposing a MCTS‑powered data‑generation pipeline to overcome the “roast chicken and baijiu” training dilemma.

GRPOMonte Carlo Tree SearchProcess Reward Model
0 likes · 14 min read
DeepSeek’s MCTS Failure: The ‘Roast Chicken and Baijiu’ Dilemma in LLM Training
Old Zhang's AI Learning
Old Zhang's AI Learning
May 4, 2026 · Artificial Intelligence

How DeepSeek’s New Paper Redefines Multimodal Reasoning with Visual Primitives

DeepSeek’s new paper "Thinking with Visual Primitives" tackles the reference gap in multimodal models by introducing points and boxes as reasoning units, achieving up to 8× token efficiency and leading benchmark scores in counting, spatial reasoning, and maze navigation compared with GPT‑5.4, Claude‑Sonnet‑4.6 and Gemini‑3‑Flash.

BenchmarkDeepSeekToken efficiency
0 likes · 10 min read
How DeepSeek’s New Paper Redefines Multimodal Reasoning with Visual Primitives
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Apr 25, 2026 · Artificial Intelligence

How Anthropic and OpenAI Monitor Frontier AI Agent Behavior – A Comprehensive Review

This article systematically reviews Anthropic and OpenAI’s public research on monitoring intelligent agent trajectories, covering infrastructure such as Clio, Petri, Bloom, chain‑of‑thought monitoring, the Confessions mechanism, internal coding‑agent audits, and the Docent tool, while highlighting mitigation strategies for reward hacking and hidden objectives.

AI AlignmentAnthropicOpenAI
0 likes · 40 min read
How Anthropic and OpenAI Monitor Frontier AI Agent Behavior – A Comprehensive Review
JavaGuide
JavaGuide
Apr 14, 2026 · Artificial Intelligence

Interview Question: How to Build Prompt Engineering for an Agent and Defend Against Malicious Prompt Injection

The article explains how industrial‑grade AI agents require structured prompt engineering, chain‑of‑thought reasoning, task decomposition, and a three‑layer defense (sandbox, prompt isolation, and human approval) to prevent prompt‑injection attacks, while also covering context engineering, retrieval‑augmented generation, and tool design best practices.

Agent DesignContext EngineeringLLM Security
0 likes · 23 min read
Interview Question: How to Build Prompt Engineering for an Agent and Defend Against Malicious Prompt Injection
Big Data and Microservices
Big Data and Microservices
Apr 12, 2026 · Artificial Intelligence

Master Structured Prompt Engineering: From Simple Commands to Powerful AI Agents

This article explains how vague AI queries lead to generic answers and shows how structured prompt engineering—using clear roles, goals, constraints, and frameworks like RTF and BROKE—can turn ambiguous business needs into precise, high‑quality AI outputs, including advanced chain‑of‑thought and few‑shot techniques for agents.

AIAgentFew-Shot
0 likes · 10 min read
Master Structured Prompt Engineering: From Simple Commands to Powerful AI Agents
Smart Workplace Lab
Smart Workplace Lab
Apr 2, 2026 · Artificial Intelligence

Master Reverse Prompt Debugging: Turn AI into Your Red‑Team Tester

Learn how to apply reverse debugging to AI prompts by letting the model act as an attacker, uncover hidden logical flaws, and use chain‑of‑thought logs to refine your instructions before they reach production, reducing costly errors and improving reliability.

AI promptingPrompt Engineeringchain-of-thought
0 likes · 3 min read
Master Reverse Prompt Debugging: Turn AI into Your Red‑Team Tester
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Mar 28, 2026 · Artificial Intelligence

How a 17‑Year‑Old Prompt Turned Claude 3.5 into a Free O1‑Level AI

A teenage prodigy engineered a "Thinking Claude" prompt that adds a human‑like chain‑of‑thought protocol to Claude 3.5, enabling free O1‑level reasoning and producing impressive outputs such as a functional calculator, sci‑fi story, and playable games, while the article details the prompt’s design process and usage.

AI reasoningClaude 3.5OpenAI o1
0 likes · 8 min read
How a 17‑Year‑Old Prompt Turned Claude 3.5 into a Free O1‑Level AI
DeepHub IMBA
DeepHub IMBA
Mar 27, 2026 · Artificial Intelligence

AI Agent Architecture: Chain‑of‑Thought, ReAct, and Tool Calls

From a simple black‑box view where an agent receives a user request and returns an answer, the article breaks down modern AI agent designs—detailing the pure Chain‑of‑Thought reasoning loop, the ReAct reasoning‑acting cycle, tool integration, iteration tuning, and how to choose the optimal architecture for production.

AI AgentsLLM architectureReact
0 likes · 9 min read
AI Agent Architecture: Chain‑of‑Thought, ReAct, and Tool Calls
AIWalker
AIWalker
Mar 19, 2026 · Artificial Intelligence

Vision‑R1 Multimodal Reasoning Model Delivers Human‑Level Logic and Near‑OpenAI O1 Accuracy

Vision‑R1 introduces a 7B multimodal large language model that leverages 200K unsupervised CoT data, Modality Bridging, and Progressive Thinking Suppression Training to overcome data scarcity and over‑thinking, achieving 73.5% accuracy on MathVista—within 0.4% of OpenAI’s O1.

Multimodal Reasoningbenchmark performancechain-of-thought
0 likes · 12 min read
Vision‑R1 Multimodal Reasoning Model Delivers Human‑Level Logic and Near‑OpenAI O1 Accuracy
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Mar 18, 2026 · Artificial Intelligence

Breaking the ‘See‑then‑Think’ Barrier: Real‑Time ‘See‑and‑Think’ for VLMs (CVPR 2026)

The paper introduces TaYS (Think‑as‑You‑See), a streaming chain‑of‑thought framework that replaces the traditional “watch‑then‑think” video inference pipeline with a parallel, real‑time “watch‑and‑think” approach, dramatically reducing latency and improving accuracy on complex video reasoning tasks.

Dual KV-CacheReal-time VideoStreaming Inference
0 likes · 8 min read
Breaking the ‘See‑then‑Think’ Barrier: Real‑Time ‘See‑and‑Think’ for VLMs (CVPR 2026)
AI Cyberspace
AI Cyberspace
Mar 10, 2026 · Artificial Intelligence

Mastering Prompt Engineering: Techniques to Guide LLMs Effectively

This article explains the fundamentals of prompt engineering for large language models, covering LLM output configuration, length and sampling controls, various prompt types, chain‑of‑thought and tree‑of‑thought reasoning methods, and practical best‑practice guidelines for creating high‑quality prompts.

AI Prompt DesignFew‑Shot LearningLLM
0 likes · 18 min read
Mastering Prompt Engineering: Techniques to Guide LLMs Effectively
AIWalker
AIWalker
Mar 5, 2026 · Artificial Intelligence

How ViDA-UGC Leverages Large Multimodal Models for Fine-Grained Visual Quality Assessment

The article introduces ViDA-UGC, a large‑scale UGC visual‑quality dataset and its companion benchmark ViDA‑Bench, explains the MILP‑driven sampling, expert annotation pipeline, and CoT‑based evaluation framework, and shows how fine‑tuning popular multimodal LLMs on this data markedly improves low‑level quality perception, grounding, and description capabilities.

BenchmarkDatasetchain-of-thought
0 likes · 12 min read
How ViDA-UGC Leverages Large Multimodal Models for Fine-Grained Visual Quality Assessment
SuanNi
SuanNi
Feb 27, 2026 · Artificial Intelligence

Can Deep Thought Ratio Reveal the True Reasoning Power of LLMs?

This article introduces the Deep Thought Ratio (DTR) metric, explains how tracking token modifications across neural network layers quantifies genuine inference effort, and shows through extensive experiments that DTR predicts accuracy far better than token length while enabling a sampling strategy that halves computational cost.

AI metricsLLM evaluationToken analysis
0 likes · 9 min read
Can Deep Thought Ratio Reveal the True Reasoning Power of LLMs?
Tencent Technical Engineering
Tencent Technical Engineering
Jan 30, 2026 · Artificial Intelligence

Can Rendering Thought Chains as Images Speed Up LLM Reasoning?

This article introduces Render‑of‑Thought (RoT), a novel paradigm that compresses chain‑of‑thought reasoning into visual embeddings using frozen vision encoders, achieving 3‑4× token reduction, faster inference, and improved interpretability while requiring minimal pre‑training.

Inference OptimizationLatent SpaceToken Compression
0 likes · 12 min read
Can Rendering Thought Chains as Images Speed Up LLM Reasoning?
Kuaishou Tech
Kuaishou Tech
Jan 28, 2026 · Artificial Intelligence

BLM‑Guard: Explainable Multimodal Ad Moderation Using Chain‑of‑Thought and Policy‑Aligned RL

The paper introduces BLM‑Guard, an explainable multimodal ad‑moderation framework that combines interleaved‑modal chain‑of‑thought reasoning with a policy‑aligned reinforcement‑learning reward to detect hidden cross‑modal violations in short‑video ads, and presents a new benchmark that demonstrates state‑of‑the‑art performance across multiple risk scenarios.

Benchmarkad risk detectionchain-of-thought
0 likes · 12 min read
BLM‑Guard: Explainable Multimodal Ad Moderation Using Chain‑of‑Thought and Policy‑Aligned RL
PaperAgent
PaperAgent
Dec 19, 2025 · Artificial Intelligence

Can We Trust AI? Inside GPT‑5.2‑Codex’s Monitorability Breakthrough

OpenAI’s new GPT‑5.2‑Codex model achieves state‑of‑the‑art performance on SWE‑Bench Pro and Terminal‑Bench 2.0, and a 90‑page technical report introduces the concept of monitorability, defining metrics, benchmark suites, and key findings about chain‑of‑thought length, RL training, and model size.

AI SafetyBenchmarkGPT-5.2
0 likes · 10 min read
Can We Trust AI? Inside GPT‑5.2‑Codex’s Monitorability Breakthrough
Bilibili Tech
Bilibili Tech
Dec 19, 2025 · Artificial Intelligence

SABER: Switchable and Balanced Training for Efficient LLM Reasoning

SABER introduces a reinforcement‑learning framework that lets large language models dynamically switch among four token‑budgeted reasoning modes, dramatically cutting inference length while preserving or improving accuracy across math, code, and logic tasks.

Budgeted ComputationEfficient ReasoningLLM
0 likes · 13 min read
SABER: Switchable and Balanced Training for Efficient LLM Reasoning
KooFE Frontend Team
KooFE Frontend Team
Dec 13, 2025 · Artificial Intelligence

Unlocking LLM Reasoning: Advanced Chain‑of‑Thought Prompting Techniques Explained

This article explains how Chain‑of‑Thought prompting and its variants—zero‑shot CoT, Thread of Thought, Tabular CoT, Analogical Prompting, and Step‑back Prompting—enable large language models to perform multi‑step reasoning by breaking problems into intermediate steps, with practical prompts, examples, and implementation details.

chain-of-thoughtreasoningzero-shot learning
0 likes · 12 min read
Unlocking LLM Reasoning: Advanced Chain‑of‑Thought Prompting Techniques Explained
Frontend AI Walk
Frontend AI Walk
Dec 5, 2025 · Artificial Intelligence

Master Prompt Engineering: From Random Chat to Precise Control with Zero-shot, Few-shot, and Chain‑of‑Thought

This article explains how to converse effectively with large language models by mastering three core prompting techniques—Zero‑shot, Few‑shot, and Chain‑of‑Thought—illustrated with front‑end analogies, code snippets, and a step‑by‑step DeepSeek JSON‑generation exercise that shows common pitfalls and best practices.

DeepSeekFew-ShotJSON generation
0 likes · 12 min read
Master Prompt Engineering: From Random Chat to Precise Control with Zero-shot, Few-shot, and Chain‑of‑Thought
Alimama Tech
Alimama Tech
Dec 3, 2025 · Artificial Intelligence

How LORE Transforms E‑Commerce Search Relevance with Generative AI

The article details the development and deployment of LORE, a large generative model that reshapes e‑commerce search relevance by combining knowledge injection, chain‑of‑thought reasoning, and multimodal alignment, achieving simultaneous improvements in user experience and revenue metrics.

Model AlignmentSystem Architecturechain-of-thought
0 likes · 15 min read
How LORE Transforms E‑Commerce Search Relevance with Generative AI
Tencent Technical Engineering
Tencent Technical Engineering
Dec 1, 2025 · Artificial Intelligence

Do Machines Really Think? Inside Deep Reasoning, Scaling Laws & RLHF for LLMs

This article examines whether large language models truly think, explores the origins of deep reasoning through transformer architectures and scaling laws, reviews chain‑of‑thought and its variants, and analyzes how reinforcement learning from human feedback—including PPO, DPO, and GRPO—helps internalise step‑by‑step reasoning while pointing to future directions such as atomic thought, hierarchical models, and training‑free in‑context knowledge bases.

AI AlignmentLLMRLHF
0 likes · 35 min read
Do Machines Really Think? Inside Deep Reasoning, Scaling Laws & RLHF for LLMs
AI Tech Publishing
AI Tech Publishing
Nov 13, 2025 · Artificial Intelligence

Claude’s Prompt Engineering Best Practices: A Step‑by‑Step Guide

This guide outlines Claude team’s best practices for prompt engineering, covering core techniques such as clear instructions, background context, specificity, examples, and advanced methods like pre‑filled responses, chain‑of‑thought, output formatting, and prompt chaining, with concrete examples and code snippets.

AI promptingClaudeContext Engineering
0 likes · 18 min read
Claude’s Prompt Engineering Best Practices: A Step‑by‑Step Guide
Amap Tech
Amap Tech
Oct 7, 2025 · Artificial Intelligence

Farsighted-LAM & SSM-VLA: Boosting Spatial‑Temporal Reasoning for Embodied AI

Introducing Farsighted-LAM, a novel latent action model that integrates geometric perception and multi‑scale temporal modeling, and its end‑to‑end SSM‑VLA framework with a Chain‑of‑Thought reasoning module, the authors demonstrate markedly improved spatial‑temporal fidelity, interpretability, and state‑of‑the‑art performance on challenging VLA benchmarks.

Embodied AIRoboticschain-of-thought
0 likes · 11 min read
Farsighted-LAM & SSM-VLA: Boosting Spatial‑Temporal Reasoning for Embodied AI
Tencent Technical Engineering
Tencent Technical Engineering
Sep 12, 2025 · Artificial Intelligence

A Structured Prompt Engineering Guide to Make LLMs Obey

Learn how to craft effective prompts for large language models by using a systematic structure—role and task, core principles, context handling, chain‑of‑thought, output specifications, and few‑shot examples—and discover techniques for generating and iteratively refining prompts with the model itself.

AI promptingFew‑Shot Learningchain-of-thought
0 likes · 10 min read
A Structured Prompt Engineering Guide to Make LLMs Obey
Data Party THU
Data Party THU
Sep 9, 2025 · Artificial Intelligence

From Chain‑of‑Thought to Graph‑of‑Thought: The Evolution of LLM Reasoning

This article examines how large language model reasoning has progressed from linear Chain‑of‑Thought prompting to parallel Tree‑of‑Thought and flexible Graph‑of‑Thought approaches, highlighting each method’s mechanism, strengths, limitations, computational costs, and the broader shift toward cognitive‑centric AI research.

AI researchGraph-of-ThoughtTree-of-Thought
0 likes · 7 min read
From Chain‑of‑Thought to Graph‑of‑Thought: The Evolution of LLM Reasoning
Data Party THU
Data Party THU
Sep 1, 2025 · Artificial Intelligence

Why Intermediate Tokens Make LLMs Reason Better: Insights from Denny Zhou

The article analyzes Denny Zhou's Stanford CS25 lecture on large language model reasoning, explaining how intermediate token generation, chain‑of‑thought prompting, self‑consistency, reinforcement‑learning fine‑tuning, and answer aggregation together unlock powerful reasoning capabilities beyond traditional greedy decoding.

AI researchLLMPrompt Engineering
0 likes · 17 min read
Why Intermediate Tokens Make LLMs Reason Better: Insights from Denny Zhou
AI2ML AI to Machine Learning
AI2ML AI to Machine Learning
Aug 25, 2025 · Artificial Intelligence

Decoding OpenAI’s Multi‑Level AGI Roadmap

The article analyzes OpenAI’s five‑layer AGI roadmap, compares it with DeepMind’s ECEVS framework, and examines the technical progress from L1 to L5—including RL‑enhanced chain‑of‑thought, ReAct agents, deep research, and upcoming innovations—while highlighting the commercial implications of each stage.

AGIDeepMindOpenAI
0 likes · 7 min read
Decoding OpenAI’s Multi‑Level AGI Roadmap
Data Thinking Notes
Data Thinking Notes
Aug 21, 2025 · Artificial Intelligence

Why Intermediate Tokens Matter: Denny Zhou’s Deep Insights into LLM Reasoning

This article distills Denny Zhou’s Stanford CS25 lecture, explaining how large language models achieve reasoning through intermediate token generation, chain‑of‑thought prompting, self‑consistency, reinforcement‑learning fine‑tuning, and answer aggregation, while highlighting theoretical foundations and practical breakthroughs.

LLMchain-of-thoughtreasoning
0 likes · 18 min read
Why Intermediate Tokens Matter: Denny Zhou’s Deep Insights into LLM Reasoning
Alibaba Cloud Developer
Alibaba Cloud Developer
Aug 18, 2025 · Artificial Intelligence

Mastering Claude Prompt Engineering: 9 Proven Strategies to Boost LLM Performance

This guide systematically breaks down Anthropic's official prompt‑engineering recommendations—clear instructions, multishot examples, chain‑of‑thought prompting, XML structuring, response pre‑filling, prompt chaining, long‑context handling, extended thinking, and practical code snippets—showing how to unlock Claude's full potential across complex tasks.

AIClaudePrompt Engineering
0 likes · 15 min read
Mastering Claude Prompt Engineering: 9 Proven Strategies to Boost LLM Performance
DaTaobao Tech
DaTaobao Tech
Aug 13, 2025 · Artificial Intelligence

Unlocking AI Power: A Complete Guide to Prompt Engineering and Advanced Techniques

This article explores the emerging field of prompt engineering, detailing its fundamentals, advanced strategies such as chain‑of‑thought, ReAct, and structured frameworks, and demonstrates practical applications in AI agents for data retrieval, SQL generation, and market insight, offering actionable guidance for developers and business users alike.

AI AgentsData RetrievalRAG
0 likes · 42 min read
Unlocking AI Power: A Complete Guide to Prompt Engineering and Advanced Techniques
Data Party THU
Data Party THU
Aug 12, 2025 · Artificial Intelligence

Unlocking Chain-of-Thought: How AI Reasoning Boosts Accuracy Across Domains

Chain‑of‑Thought (CoT) enables large language models to solve complex tasks by breaking problems into sequential reasoning steps, improving accuracy in mathematics, commonsense, code generation, business strategy, and medical diagnosis, while highlighting its principles, advantages, challenges, and future prospects.

LLMPrompt Designchain-of-thought
0 likes · 13 min read
Unlocking Chain-of-Thought: How AI Reasoning Boosts Accuracy Across Domains
JD Retail Technology
JD Retail Technology
Jul 21, 2025 · Artificial Intelligence

How Causal Inference Meets Large Language Models to Revolutionize E‑commerce Pricing

This article presents a comprehensive approach that combines causal inference, large language models, and retrieval‑augmented generation to automate e‑commerce price recommendation, detailing the three‑step workflow, challenges across product categories, the RAG architecture, process‑reward‑guided tree search, reinforcement learning refinements, and experimental results showing significant accuracy and speed improvements.

causal inferencechain-of-thoughte‑commerce pricing
0 likes · 16 min read
How Causal Inference Meets Large Language Models to Revolutionize E‑commerce Pricing
Kuaishou Tech
Kuaishou Tech
Jul 16, 2025 · Artificial Intelligence

How KuaiMM Conversation Revolutionizes Multimodal Dialogue on Short‑Video Platforms

The KuaiMM Conversation project introduces a multimodal large‑model‑driven dialogue system for Kuaishou, featuring the world‑first short‑video mixed‑dialogue dataset, a Chain‑of‑Thought interaction framework, and large‑scale industrial deployments that dramatically improve live‑stream comments and intelligent customer service.

DatasetKuaishouchain-of-thought
0 likes · 11 min read
How KuaiMM Conversation Revolutionizes Multimodal Dialogue on Short‑Video Platforms
Alimama Tech
Alimama Tech
Jul 9, 2025 · Artificial Intelligence

How to Make LLMs Recognize and Resolve Their Own Uncertainty

This article introduces ConfuseBench, a benchmark that classifies LLM uncertainty into document‑missing, ability‑limited, and ambiguous types, and presents methods—including retrieval, chain‑of‑thought, and clarification—to detect and actively resolve uncertainty, improving answer quality across diverse tasks.

BenchmarkClarificationInquiry
0 likes · 17 min read
How to Make LLMs Recognize and Resolve Their Own Uncertainty
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Jun 30, 2025 · Artificial Intelligence

Unlocking Small LLM Power: Variable‑Length Chain Distillation with DistillQwen‑ThoughtY

This article introduces a variable‑length chain‑of‑thought distillation technique built on Alibaba Cloud PAI’s EasyDistill toolkit, presents the high‑quality OmniThought‑0528 dataset, details the training of the DistillQwen‑ThoughtY 4B/8B/32B models, and provides code and usage examples for researchers and practitioners.

DatasetDistillationLLM
0 likes · 15 min read
Unlocking Small LLM Power: Variable‑Length Chain Distillation with DistillQwen‑ThoughtY
Xiaohongshu Tech REDtech
Xiaohongshu Tech REDtech
Jun 19, 2025 · Artificial Intelligence

Can Adaptive Chain‑of‑Thought Learning Halve LLM Thinking Time?

The article introduces the Think When You Need (TWYN) method, a reinforcement‑learning approach that dynamically adapts chain‑of‑thought length, dramatically cuts redundant token generation in large language models, and maintains or improves accuracy across diverse reasoning benchmarks.

adaptive inferencechain-of-thoughtefficiency
0 likes · 9 min read
Can Adaptive Chain‑of‑Thought Learning Halve LLM Thinking Time?
Fighter's World
Fighter's World
Jun 14, 2025 · Artificial Intelligence

How Can LLMs Learn to “Think” in Complex Industry Scenarios?

The article analyzes how large language models can acquire true reasoning abilities for hard‑to‑score industry tasks by combining Chain‑of‑Thought prompting with reinforcement learning, addressing vague reward signals, reward hacking, and loyalty, and proposing a toolbox of reward engineering, synthetic data, hierarchical RL and multi‑agent collaboration.

LLMReward Modelingchain-of-thought
0 likes · 22 min read
How Can LLMs Learn to “Think” in Complex Industry Scenarios?
Code Mala Tang
Code Mala Tang
Jun 5, 2025 · Artificial Intelligence

Mastering LLM Prompts: Proven Techniques to Get Precise Answers

By rethinking how we interact with large language models—using role‑play, task decomposition, chain‑of‑thought, ReAct, and other advanced prompting strategies—readers can transform generic ChatGPT answers into precise, context‑aware responses, leveraging pattern recognition and context windows for superior AI assistance.

AI reasoningLLM techniquesPrompt Engineering
0 likes · 21 min read
Mastering LLM Prompts: Proven Techniques to Get Precise Answers
AI Frontier Lectures
AI Frontier Lectures
May 30, 2025 · Artificial Intelligence

Can Diffusion Chains Unlock More Creative Reasoning in Large Language Models?

Recent work from West Lake University's MAPLE Lab introduces a diffusion‑based “Divergent Thought Chain” that treats each intermediate denoising step of a diffusion language model as a reasoning step, using result‑based reinforcement learning to optimize non‑linear token generation and achieving state‑of‑the‑art performance on math and code tasks.

Code Generationchain-of-thoughtdiffusion language models
0 likes · 14 min read
Can Diffusion Chains Unlock More Creative Reasoning in Large Language Models?
Efficient Ops
Efficient Ops
May 29, 2025 · Artificial Intelligence

DeepSeek R1 0528 Update: New Features, Performance Gains Over OpenAI o3

DeepSeek quietly launched the R1 0528 model, which early testers report matches OpenAI’s o3 in benchmarks and style, while adding deeper chain‑of‑thought reasoning, better writing output, and extended thinking windows, and the announcement is followed by a promotion for the GOPS Global Ops Conference.

AI PerformanceDeepSeekModel Update
0 likes · 3 min read
DeepSeek R1 0528 Update: New Features, Performance Gains Over OpenAI o3
AI Frontier Lectures
AI Frontier Lectures
May 25, 2025 · Artificial Intelligence

Can Alternating Generation‑Reduction Make LLMs Think Faster? Introducing PENCIL

The paper presents PENCIL, a novel alternating generation‑and‑erasure reasoning paradigm that achieves optimal space‑time complexity for chain‑of‑thought tasks, dramatically improves accuracy and efficiency on hard SAT, QBF, and Einstein puzzle benchmarks, and is provably Turing‑complete.

Benchmark resultsPencilchain-of-thought
0 likes · 12 min read
Can Alternating Generation‑Reduction Make LLMs Think Faster? Introducing PENCIL
Alimama Tech
Alimama Tech
Apr 23, 2025 · Artificial Intelligence

Explainable LLM-driven Multi-dimensional Distillation for E-Commerce Relevance Learning

The paper introduces an explainable LLM framework (ELLM‑rele) that uses chain‑of‑thought reasoning and a multi‑dimensional knowledge distillation pipeline to compress large‑model relevance judgments into lightweight student models, achieving superior offline relevance scores and online click‑through and conversion improvements in Taobao’s search advertising.

LLMchain-of-thoughtexplainability
0 likes · 17 min read
Explainable LLM-driven Multi-dimensional Distillation for E-Commerce Relevance Learning
Alibaba Cloud Big Data AI Platform
Alibaba Cloud Big Data AI Platform
Apr 22, 2025 · Artificial Intelligence

How DistilQwen2.5-DS3-0324 Achieves Fast, Accurate Reasoning via Quick‑Think Distillation

This article introduces DistilQwen2.5-DS3-0324, a distilled language model series that balances rapid inference with strong reasoning by applying a fast‑thinking chain‑of‑thought strategy, details its two‑stage distillation framework, evaluation on diverse benchmarks, and provides code for downloading and using the models.

Deep Learningchain-of-thoughtfast inference
0 likes · 17 min read
How DistilQwen2.5-DS3-0324 Achieves Fast, Accurate Reasoning via Quick‑Think Distillation
Amap Tech
Amap Tech
Apr 21, 2025 · Artificial Intelligence

Lenna: Language‑Enhanced Reasoning Detection Assistant and a Chain‑of‑Thought Image Editing Framework Using Multimodal Large Language Models

At ICASSP 2025, Gaode’s two accepted papers present Lenna, a language‑enhanced reasoning detection assistant that adds a DET token to multimodal LLMs and achieves state‑of‑the‑art accuracy on RefCOCO benchmarks, and a chain‑of‑thought image‑editing framework that converts complex prompts into segmented masks and repair prompts for diffusion‑based inpainting, surpassing existing methods.

AIComputer VisionICASSP
0 likes · 10 min read
Lenna: Language‑Enhanced Reasoning Detection Assistant and a Chain‑of‑Thought Image Editing Framework Using Multimodal Large Language Models
AI Algorithm Path
AI Algorithm Path
Apr 20, 2025 · Artificial Intelligence

Boosting Visual Reasoning in VLMs with Reinforcement Learning

The article analyzes how reinforcement learning, which transformed LLM reasoning in DeepSeek, can be applied to visual‑language models to overcome the limitations of traditional chain‑of‑thought prompting and supervised fine‑tuning, presenting concrete reward designs, training pipelines, and a critical assessment of their strengths and weaknesses.

LLMRL trainingVisual-Language Models
0 likes · 10 min read
Boosting Visual Reasoning in VLMs with Reinforcement Learning
Alibaba Cloud Developer
Alibaba Cloud Developer
Apr 9, 2025 · Artificial Intelligence

Unlocking LLM Reasoning: A Deep Dive into Prompt Engineering Techniques

This article surveys classic prompt‑engineering methods such as Chain‑of‑Thought, Self‑Consistency, Least‑to‑Most, Boosting of Thoughts, Tree of Thoughts, and AutoGPT, summarizing their core ideas, advantages, limitations, and experimental results to help readers understand how to enhance large language model reasoning without model fine‑tuning.

AI reasoningSelf-Consistencychain-of-thought
0 likes · 22 min read
Unlocking LLM Reasoning: A Deep Dive into Prompt Engineering Techniques
DataFunTalk
DataFunTalk
Mar 24, 2025 · Artificial Intelligence

DeepSeek R1: Open‑Source Reasoning Model and Multi‑Stage Training Insights

The interview explores DeepSeek R1's open‑source weights, its multi‑stage training pipeline—including pre‑training, supervised fine‑tuning, and RLHF—alongside innovations such as self‑consistency, chain‑of‑thought prompting, distillation, MoE architectures, and cost considerations, highlighting its impact on the future of large language models.

AI trainingDeepSeekRLHF
0 likes · 20 min read
DeepSeek R1: Open‑Source Reasoning Model and Multi‑Stage Training Insights
AI Frontier Lectures
AI Frontier Lectures
Mar 21, 2025 · Artificial Intelligence

Can Chain‑of‑Thought Templates Unlock Higher Reasoning Limits in LLMs?

The article examines how chain‑of‑thought (CoT) templates are evolving from short‑term heuristics to long‑range planning in large language models, highlighting recent advances such as OpenAI o1, DeepSeek R1, and Kimi 1.5, and explores template designs that boost reasoning performance, efficiency, and multimodal capabilities.

AI reasoningLong CoTPrompt Engineering
0 likes · 7 min read
Can Chain‑of‑Thought Templates Unlock Higher Reasoning Limits in LLMs?
Architect
Architect
Mar 16, 2025 · Artificial Intelligence

Training a 0.5B LLM with Chain‑of‑Thought Reasoning: From Pre‑training to GRPO Fine‑tuning

This article walks through the complete lifecycle of building a small large‑language model, covering token‑level inference, pre‑training, post‑training steps such as supervised fine‑tuning, reward‑model creation, and reinforcement‑learning methods like DPO, PPO and GRPO, culminating in a practical 0.5B model fine‑tuned for chain‑of‑thought reasoning.

GRPOLLM trainingReward Modeling
0 likes · 22 min read
Training a 0.5B LLM with Chain‑of‑Thought Reasoning: From Pre‑training to GRPO Fine‑tuning
Selected Java Interview Questions
Selected Java Interview Questions
Mar 15, 2025 · Artificial Intelligence

DeepSeek4j 1.4: Java Spring Boot Integration for DeepSeek with Full Chain‑of‑Thought and Streaming Support

DeepSeek4j 1.4 introduces a Java‑centric, Spring Boot‑compatible framework that fully preserves DeepSeek's chain‑of‑thought capabilities, adds reactive streaming, and provides simple one‑line API integration, addressing previous limitations in mainstream frameworks and offering ready‑to‑use configuration and code examples.

AI integrationDeepSeekSpring Boot
0 likes · 5 min read
DeepSeek4j 1.4: Java Spring Boot Integration for DeepSeek with Full Chain‑of‑Thought and Streaming Support
Fun with Large Models
Fun with Large Models
Mar 8, 2025 · Artificial Intelligence

Make AI Obey: A Detailed Prompt Engineering Guide to Boost Large‑Model Logic

This tutorial explains how to enhance large language models' logical reasoning by using DeepSeek‑R1's deep‑thinking mode, few‑shot prompting, chain‑of‑thought, and zero‑shot chain‑of‑thought techniques, providing concrete examples, comparisons, and a step‑by‑step template for effective prompt design.

AI reasoningDeepSeekchain-of-thought
0 likes · 10 min read
Make AI Obey: A Detailed Prompt Engineering Guide to Boost Large‑Model Logic
Java Architect Essentials
Java Architect Essentials
Mar 7, 2025 · Artificial Intelligence

Introducing DeepSeek4j 1.4: A Java Spring Boot Integration for DeepSeek AI with Chain‑of‑Thought and Streaming Support

The article introduces DeepSeek4j 1.4, a Java Spring Boot library that overcomes existing framework limitations by preserving DeepSeek's chain‑of‑thought capabilities, adding full reactive streaming, and providing a simple one‑line API along with quick‑start instructions and code examples.

AI integrationDeepSeekJava
0 likes · 5 min read
Introducing DeepSeek4j 1.4: A Java Spring Boot Integration for DeepSeek AI with Chain‑of‑Thought and Streaming Support
AI Code to Success
AI Code to Success
Mar 6, 2025 · Artificial Intelligence

Inside the Brain Module: How AI Agents Process, Remember, and Decide

This article provides a comprehensive analysis of the Brain module in AI agents, covering its multi‑step workflow, knowledge integration, memory mechanisms, intent recognition, planning strategies, reasoning techniques, and the role of reflection and emotion in enhancing adaptability and robustness.

Brain moduleEmotion simulationMemory Architecture
0 likes · 17 min read
Inside the Brain Module: How AI Agents Process, Remember, and Decide
Code Mala Tang
Code Mala Tang
Feb 27, 2025 · Artificial Intelligence

Do New AI Reasoning Models Really Think? Unpacking the Debate

The article examines whether the latest AI models that claim to perform true reasoning—by breaking problems into steps and using chain‑of‑thought—actually reason like humans, presenting skeptical and supportive expert viewpoints, and offering practical guidance on how to use such models responsibly.

AI SafetyAI reasoningchain-of-thought
0 likes · 14 min read
Do New AI Reasoning Models Really Think? Unpacking the Debate
Java Web Project
Java Web Project
Feb 25, 2025 · Artificial Intelligence

How DeepSeek4j 1.4 Solves Spring AI’s Chain‑of‑Thought and Streaming Gaps

The article explains why existing Java AI frameworks struggle with DeepSeek R1’s chain‑of‑thought and streaming features, introduces DeepSeek4j 1.4 as a targeted solution, details its core capabilities, and provides a step‑by‑step guide to integrate it with Spring Boot and Project Reactor.

AI integrationDeepSeekJava
0 likes · 5 min read
How DeepSeek4j 1.4 Solves Spring AI’s Chain‑of‑Thought and Streaming Gaps
Top Architect
Top Architect
Feb 21, 2025 · Artificial Intelligence

DeepSeek4j 1.4: Java Integration Framework for DeepSeek with Full Chain‑of‑Thought and Streaming Support

The article introduces DeepSeek4j 1.4, a Java‑based framework that overcomes Spring AI’s limitations by fully preserving DeepSeek’s chain‑of‑thought and billing features, adding reactive streaming, providing Spring Boot starter integration, and offering quick‑start code samples and configuration guidance.

AIDeepSeekJava
0 likes · 8 min read
DeepSeek4j 1.4: Java Integration Framework for DeepSeek with Full Chain‑of‑Thought and Streaming Support
Tencent Technical Engineering
Tencent Technical Engineering
Feb 17, 2025 · Artificial Intelligence

Prompt Engineering: Definitions, Frameworks, Principles, and Advanced Techniques

The guide defines prompts as structured queries that unlock large‑language‑model abilities, outlines five core frameworks (RTF, Chain‑of‑Thought, RISEN, RODES, Density‑Chain), presents two key principles—clear, delimited instructions and explicit reasoning steps—to reduce hallucinations, and surveys advanced techniques such as zero‑shot, few‑shot, RAG, Tree‑of‑Thought and automatic prompt engineering.

AIRetrieval Augmented Generationchain-of-thought
0 likes · 29 min read
Prompt Engineering: Definitions, Frameworks, Principles, and Advanced Techniques
AI Algorithm Path
AI Algorithm Path
Feb 12, 2025 · Artificial Intelligence

Essential DeepSeek‑R1 Reading List: Papers Behind the 2025 Hottest LLM

This article compiles a curated reading list of foundational and recent research papers—from the original Transformer to chain‑of‑thought, mixture‑of‑experts, and reinforcement‑learning studies—that together explain the breakthroughs behind DeepSeek‑R1 and guide readers through the technical evolution of modern large language models.

DeepSeekMixture of ExpertsResearch Papers
0 likes · 15 min read
Essential DeepSeek‑R1 Reading List: Papers Behind the 2025 Hottest LLM
Java Architecture Diary
Java Architecture Diary
Feb 5, 2025 · Artificial Intelligence

Unlocking DeepSeek R1’s Chain‑of‑Thought: A Spring WebFlux Integration Guide

This article examines why mainstream AI frameworks like Spring AI and LangChain4j cannot fully support DeepSeek’s R1 model, explains its unique chain‑of‑thought response format and parameter constraints, and provides a complete Spring WebFlux‑based solution—including API calls, streaming handling, and response parsing—to preserve reasoning content.

DeepSeekR1chain-of-thought
0 likes · 8 min read
Unlocking DeepSeek R1’s Chain‑of‑Thought: A Spring WebFlux Integration Guide
AIWalker
AIWalker
Feb 4, 2025 · Artificial Intelligence

How Chain‑of‑Thought Boosts Text‑to‑Image Generation: The New o1 Inference Scheme

This article reviews a comprehensive study that applies Chain‑of‑Thought reasoning to autoregressive text‑to‑image generation, introducing extended test‑time computation, direct preference optimization, and two custom reward models (PARM and PARM++) that together improve generation quality by up to 15% over Stable Diffusion 3.

Direct Preference OptimizationInferenceReward model
0 likes · 13 min read
How Chain‑of‑Thought Boosts Text‑to‑Image Generation: The New o1 Inference Scheme
DaTaobao Tech
DaTaobao Tech
Jan 24, 2025 · Artificial Intelligence

MktAI Assistant: AI‑Driven Marketing Data Query and Insight Platform

The MktAI Assistant combines LLM‑powered memory, skill planning, and tool‑calling with real‑time API data to replace slow, manual SQL dashboards, delivering sub‑minute, fresh, explainable marketing queries and attribution insights that boost decision speed, accuracy, and collaboration between data scientists and business users.

AI AgentData ScienceFunction Calling
0 likes · 16 min read
MktAI Assistant: AI‑Driven Marketing Data Query and Insight Platform
Baobao Algorithm Notes
Baobao Algorithm Notes
Nov 24, 2024 · Artificial Intelligence

How Marco‑o1 Merges Chain‑of‑Thought Fine‑Tuning with Monte‑Carlo Tree Search for Superior Reasoning

The article introduces Marco‑o1, an open‑source LLM that enhances complex reasoning by fine‑tuning on Chain‑of‑Thought data, integrating Monte‑Carlo Tree Search, introducing mini‑step actions and a reflection mechanism, and evaluates its performance on multilingual math and translation benchmarks.

LLMMonte Carlo Tree Searchartificial intelligence
0 likes · 15 min read
How Marco‑o1 Merges Chain‑of‑Thought Fine‑Tuning with Monte‑Carlo Tree Search for Superior Reasoning
JD Tech
JD Tech
Nov 12, 2024 · Artificial Intelligence

Prompt Engineering: Concepts, Evolution, Techniques, and JD Logistics Application

This article explains what Prompt Engineering is, traces its development from early NLP commands to modern adaptive and multimodal prompting techniques, describes various prompting strategies such as Zero‑shot, Few‑shot, Chain‑of‑Thought, Auto‑CoT, and showcases a JD Logistics case study using these methods to classify product types with code examples.

AI Prompt DesignFew-ShotPrompt Engineering
0 likes · 27 min read
Prompt Engineering: Concepts, Evolution, Techniques, and JD Logistics Application
JD Tech Talk
JD Tech Talk
Nov 11, 2024 · Artificial Intelligence

Prompt Engineering: Concepts, Evolution, Techniques, and a Logistics Application Case

This article explains what Prompt Engineering is, traces its development from early command‑based interactions to modern adaptive and multimodal prompting, details various prompting techniques such as zero‑shot, few‑shot, Chain‑of‑Thought, hallucination‑reduction methods, and demonstrates their practical use in a JD Logistics SKU piece‑type classification case with code examples.

AI promptingFew‑Shot LearningLLM applications
0 likes · 26 min read
Prompt Engineering: Concepts, Evolution, Techniques, and a Logistics Application Case
JD Cloud Developers
JD Cloud Developers
Nov 11, 2024 · Artificial Intelligence

Mastering Prompt Engineering: History, Techniques, and Real-World Applications

This article explains what Prompt Engineering is, traces its evolution from early NLP commands to modern adaptive and multimodal prompting, details core techniques such as Zero‑shot, Chain‑of‑Thought, Auto‑CoT, and reduction of hallucinations, and showcases a logistics case study using various prompting strategies.

AILLMPrompt Design
0 likes · 26 min read
Mastering Prompt Engineering: History, Techniques, and Real-World Applications
DaTaobao Tech
DaTaobao Tech
Oct 30, 2024 · Artificial Intelligence

Understanding OpenAI o1: Chain‑of‑Thought, Scaling Laws, and Training Strategies

The article explains how OpenAI’s o1 model leverages chain‑of‑thought prompting, dual‑system cognitive theory, and new scaling laws—pre‑training on code/math and post‑training reinforcement with step‑wise reward models—to achieve superior reasoning, safety, and performance over GPT‑4, heralding a shift toward models that learn to think.

LLMSafetychain-of-thought
0 likes · 42 min read
Understanding OpenAI o1: Chain‑of‑Thought, Scaling Laws, and Training Strategies
Architect
Architect
Sep 26, 2024 · Artificial Intelligence

Decoding OpenAI o1: How RL‑LLM Fusion Powers Next‑Gen Reasoning

This article provides a detailed technical analysis of OpenAI’s o1 model, exploring its enhanced logical reasoning, the likely use of reinforcement learning with hidden chain‑of‑thought generation, multi‑model architecture, training data pipelines, reward modeling, and how these innovations could reshape AI safety and scaling strategies.

AI SafetyLLMModel architecture
0 likes · 43 min read
Decoding OpenAI o1: How RL‑LLM Fusion Powers Next‑Gen Reasoning
iQIYI Technical Product Team
iQIYI Technical Product Team
Sep 26, 2024 · Artificial Intelligence

AI-Powered Search in iQIYI: Techniques, Architecture, and Implementation

iQIYI’s AI‑powered search expands beyond title‑only queries by handling fuzzy role, plot, star, award, and semantic searches, using Chain‑of‑Thought‑generated TIPS, Retrieval‑Augmented Generation with sophisticated indexing, chunking, embedding, reranking, and prompt‑engineering to deliver personalized, accurate video recommendations that boost user engagement.

AI searchEmbeddingQuery Guidance
0 likes · 15 min read
AI-Powered Search in iQIYI: Techniques, Architecture, and Implementation
CSS Magic
CSS Magic
Sep 14, 2024 · Artificial Intelligence

Why OpenAI’s New o1 Model Outperforms Its Rivals

The article examines OpenAI’s newly released o1 model, highlighting its superior performance in complex reasoning tasks such as math, programming, and science, and explains how model‑level chain‑of‑thought optimization and product‑level UI design give it an edge over competitors like Claude.

AI EvaluationChatGPTOpenAI
0 likes · 8 min read
Why OpenAI’s New o1 Model Outperforms Its Rivals
MaGe Linux Operations
MaGe Linux Operations
Sep 13, 2024 · Artificial Intelligence

Can OpenAI’s New o1 Model Reach Human‑Level Reasoning?

OpenAI’s newly released o1 series introduces a reinforcement‑learning‑trained LLM that generates long chain‑of‑thought reasoning, achieving top‑50% scores on IOI contests, high rankings on Codeforces and AIME, and dramatically outperforming GPT‑4o across scientific and mathematical tasks.

AI reasoningOpenAIartificial intelligence
0 likes · 8 min read
Can OpenAI’s New o1 Model Reach Human‑Level Reasoning?
Tencent Cloud Developer
Tencent Cloud Developer
Jul 30, 2024 · Artificial Intelligence

A Systematic Guide to Prompt Engineering: From Zero to One

This guide walks readers from beginner to proficient Prompt Engineer by outlining the evolution of prompting, introducing a universal four‑component template, and detailing a five‑step workflow—including refinement, retrieval‑augmented generation, chain‑of‑thought reasoning, and advanced tuning techniques—plus evaluation metrics for LLM performance.

AI promptingLLM optimizationPrompt Engineering
0 likes · 51 min read
A Systematic Guide to Prompt Engineering: From Zero to One
NewBeeNLP
NewBeeNLP
Jun 19, 2024 · Artificial Intelligence

Can Symbolic Chain‑of‑Thought Boost LLM Logical Reasoning?

The paper introduces SymbCoT, a Symbolic Chain‑of‑Thought framework that translates natural‑language problems into symbolic form, plans, solves, and verifies reasoning steps, achieving significantly higher logical reasoning performance than traditional CoT methods across multiple benchmark datasets.

ACL 2024LLMLogical Reasoning
0 likes · 13 min read
Can Symbolic Chain‑of‑Thought Boost LLM Logical Reasoning?
Xiaohongshu Tech REDtech
Xiaohongshu Tech REDtech
Apr 10, 2024 · Artificial Intelligence

Early‑Stopping Self‑Consistency (ESC): Reducing Sampling Cost for Large Language Model Reasoning

Early‑Stopping Self‑Consistency (ESC) dynamically halts sampling once a sliding‑window answer distribution reaches zero entropy, cutting the number of required LLM reasoning samples by 34‑84 % across arithmetic, commonsense, and symbolic benchmarks while preserving accuracy and offering a theoretically‑bounded, robust, budget‑adaptive alternative to traditional Self‑Consistency.

AIEarly StoppingInference
0 likes · 14 min read
Early‑Stopping Self‑Consistency (ESC): Reducing Sampling Cost for Large Language Model Reasoning
DataFunTalk
DataFunTalk
Feb 2, 2024 · Artificial Intelligence

Utilizing Negative Samples for Knowledge Distillation of Large Language Models

This paper presents a novel framework that leverages negative samples during large language model distillation through three stages—Negative Assistive Training, Negative Calibration Enhancement, and Adaptive Self‑Consistency—demonstrating significant accuracy gains on challenging mathematical reasoning benchmarks and improved generalization to out‑of‑distribution tasks.

Knowledge TransferLLM distillationchain-of-thought
0 likes · 13 min read
Utilizing Negative Samples for Knowledge Distillation of Large Language Models
Sohu Tech Products
Sohu Tech Products
Aug 23, 2023 · Artificial Intelligence

Engineering GPT Applications: Capabilities, Limitations, and Solutions

The guide explains GPT’s core capabilities—natural language mastery, domain reasoning, and code generation—while detailing its limits such as prompt sensitivity, token caps, and lack of memory, then offers engineering workarounds like systematic prompting, chain‑of‑thought, external memory, tool integration, safety checks, and a six‑layer architecture for building robust commercial AI applications.

AI Application ArchitectureGPTPrompt Engineering
0 likes · 20 min read
Engineering GPT Applications: Capabilities, Limitations, and Solutions
Alibaba Cloud Developer
Alibaba Cloud Developer
Jul 19, 2023 · Artificial Intelligence

Mastering Prompt Engineering: Techniques, Tips, and Real-World Examples

This comprehensive guide explores prompt engineering for large language models, covering its background, fundamental concepts, prompt formats, construction principles, advanced techniques like few‑shot, zero‑shot, and chain‑of‑thought prompting, as well as practical examples, evaluation metrics, and future directions.

Few-ShotLLMPrompt Engineering
0 likes · 33 min read
Mastering Prompt Engineering: Techniques, Tips, and Real-World Examples
Tencent Cloud Developer
Tencent Cloud Developer
Jun 28, 2023 · Artificial Intelligence

Prompt Engineering: Fundamentals, Techniques, and Advanced Strategies

Prompt engineering teaches how to craft effective instructions, context, input data, and output formats for large language models, using clear commands, iterative refinement, and advanced methods such as zero‑shot, few‑shot, chain‑of‑thought, Tree of Thoughts, retrieval‑augmented and progressive‑hint prompting to achieve precise, reliable results across diverse tasks.

AIFew‑Shot LearningKnowledge Retrieval
0 likes · 17 min read
Prompt Engineering: Fundamentals, Techniques, and Advanced Strategies
ByteFE
ByteFE
Jun 15, 2023 · Artificial Intelligence

Effective Prompt Engineering: Techniques, Prompt Injection Prevention, Hallucination Mitigation, and Advanced Prompting Strategies

This article explains how to craft efficient prompts by combining clear instructions and questions, discusses prompt injection risks and mitigation with delimiters, addresses hallucinations, and introduces zero‑shot, few‑shot, and chain‑of‑thought prompting techniques for large language models.

Few-ShotLLMPrompt Engineering
0 likes · 16 min read
Effective Prompt Engineering: Techniques, Prompt Injection Prevention, Hallucination Mitigation, and Advanced Prompting Strategies
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
DataFunSummit
DataFunSummit
Mar 19, 2023 · Artificial Intelligence

Complex Question Answering Evaluation of ChatGPT

This paper presents a large‑scale evaluation of ChatGPT on knowledge‑base complex question answering, introducing a feature‑driven multi‑label annotation framework and CheckList‑based functional, robustness, and controllability tests, and comparing its performance with other LLMs across multiple English and multilingual datasets.

ChatGPTComplex QAchain-of-thought
0 likes · 25 min read
Complex Question Answering Evaluation of ChatGPT
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 10, 2023 · Artificial Intelligence

Why ChatGPT Shows Strong General Intelligence: Insights from Andrew Ng’s DeepLearning.AI Article

The article explains how techniques such as Reinforcement Learning from Human Feedback, Instruction Fine‑Tuning, Supervised Fine‑tuning and Chain‑of‑Thought contribute to ChatGPT’s impressive general‑intelligence performance, as analyzed by DeepLearning.AI founder Andrew Ng.

ChatGPTDeepLearning.AIReinforcement Learning from Human Feedback
0 likes · 2 min read
Why ChatGPT Shows Strong General Intelligence: Insights from Andrew Ng’s DeepLearning.AI Article