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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

JD Tech

Aug 20, 2025 · Artificial Intelligence

Boosting Text-to-SQL Accuracy with J‑Schema, Iterative DPO, and Self‑Consistency

This article examines the evolution of Text-to-SQL, introduces the J‑Schema representation and chain-of-thought prompting, applies iterative DPO training and self-consistency voting, and demonstrates how these techniques raise execution accuracy on the BIRD benchmark from 56.6% to 69.2%.

BIRD benchmarkIterative DPOJ-Schema

0 likes · 11 min read

Boosting Text-to-SQL Accuracy with J‑Schema, Iterative DPO, and Self‑Consistency

JD Tech Talk

Aug 14, 2025 · Artificial Intelligence

Boosting Text-to-SQL Accuracy: J‑Schema, Iterative DPO, and Self‑Consistency

This paper presents a comprehensive approach to improve Text‑to‑SQL performance by introducing J‑Schema for structured database representation, leveraging chain‑of‑thought prompting, applying iterative DPO training, and employing self‑consistency voting, achieving execution accuracy gains from 56.6% to 69.2% on the BIRD benchmark.

BIRD benchmarkIterative DPOSelf-Consistency

0 likes · 12 min read

Boosting Text-to-SQL Accuracy: J‑Schema, Iterative DPO, and Self‑Consistency

JD Cloud Developers

Aug 14, 2025 · Artificial Intelligence

Boosting Text-to-SQL Accuracy: J‑Schema, Iterative DPO, and Self‑Consistency

This article presents a comprehensive study on improving Text-to-SQL performance by introducing J‑Schema for structured schema representation, applying iterative Direct Preference Optimization (DPO) training, and leveraging self‑consistency voting mechanisms, achieving up to a 12% accuracy gain on the BIRD benchmark.

Database QAIterative DPOJ-Schema

0 likes · 10 min read

JD Retail Technology

Aug 14, 2025 · Artificial Intelligence

Boosting Text-to-SQL Accuracy: J‑Schema, Iterative DPO, and Self‑Consistency

This article surveys the evolution of Text-to-SQL, introduces the J‑Schema representation and chain-of-thought prompting, details an iterative DPO training pipeline with hyper‑parameter tuning, and demonstrates how self‑consistency voting boosts execution accuracy on the BIRD benchmark from 56.6% to 69.2%.

BIRD datasetIterative DPOLLM

0 likes · 14 min read

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

Xiaohongshu Tech REDtech

Jun 20, 2024 · Artificial Intelligence

Xiaohongshu 2024 Large Model Frontier Paper Sharing Live Event

On June 27, 2024, Xiaohongshu’s technical team will livestream a two‑hour session across WeChat Channels, Bilibili, Douyin and Xiaohongshu, showcasing six top‑conference papers on large‑model advances—including early‑stopping and fine‑grained self‑consistency, novel evaluation methods, negative‑sample‑assisted distillation, and LLM‑based note recommendation—followed by a Q&A and recruitment briefing.

AI researchModel EvaluationRecommendation Systems

0 likes · 12 min read

Xiaohongshu 2024 Large Model Frontier Paper Sharing Live Event