Tagged articles

chain-of-thought

124 articles · Page 2 of 2

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 OptimizationReward Modelchain-of-thought

0 likes · 13 min read

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

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

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.

Artificial IntelligenceLLMMonte Carlo Tree Search

0 likes · 15 min read

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

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-shotchain-of-thought

0 likes · 27 min read

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

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

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

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.

LLMReinforcement Learningchain-of-thought

0 likes · 42 min read

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

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 safetyLLMOpenAI o1

0 likes · 43 min read

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

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 SearchQuery GuidanceRAG

0 likes · 15 min read

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

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

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 reasoningArtificial IntelligenceOpenAI

0 likes · 8 min read

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

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 OptimizationRAG

0 likes · 51 min read

A Systematic Guide to Prompt Engineering: From Zero to One

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 2024LLMchain-of-thought

0 likes · 13 min read

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

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.

AILLMchain-of-thought

0 likes · 14 min read

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

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.

LLM distillationchain-of-thoughtknowledge transfer

0 likes · 13 min read

Utilizing Negative Samples for Knowledge Distillation of Large Language Models

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 architectureGPTchain-of-thought

0 likes · 20 min read

Engineering GPT Applications: Capabilities, Limitations, and Solutions

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.

Artificial IntelligenceFew-shotLLM

0 likes · 33 min read

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

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

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-shotLLMchain-of-thought

0 likes · 16 min read

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

Python Crawling & Data Mining

May 7, 2023 · Artificial Intelligence

Why Does ChatGPT Suddenly ‘Think Step‑by‑Step’? Unveiling the Chain‑of‑Thought Emergence

The article explains how ChatGPT’s surprising step‑by‑step reasoning, known as Chain of Thought, emerged as a technical breakthrough, links it to model scaling, cognitive System 1/2 theory, and the influence of code data in training large language models.

AI reasoningChatGPTCognitive Science

0 likes · 10 min read

Why Does ChatGPT Suddenly ‘Think Step‑by‑Step’? Unveiling the Chain‑of‑Thought Emergence

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 researchGrokkingchain-of-thought

0 likes · 13 min read

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

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.

ChatGPTChecklistComplex QA

0 likes · 25 min read

Complex Question Answering Evaluation of ChatGPT

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.

Artificial IntelligenceChatGPTRLHF

0 likes · 15 min read

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

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.

Artificial IntelligenceChatGPTDeepLearning.AI

0 likes · 2 min read

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