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Research papers, made simple to understand
The AI and machine learning papers people are reading most, each one rewritten in plain words: the problem, the idea, what was found and why it matters.
- LLM Reasoning
Chain-of-thought prompting improves reasoning in large language models
Providing large language models with few-shot exemplars that include intermediate reasoning steps, called chain-of-thought prompting, significantly boosts their performance on arithmetic, commonsense, and symbolic reasoning tasks, achieving state-of-the-art results without finetuning.
Paper: Chain-of-Thought Prompting Elicits Reasoning in Large Language Models · Jason Wei, Xuezhi Wang, Dale Schuurmans and 6 others
Classic · 20225 min readRead the article
- Retrieval & RAG
Combining retrieval and generation improves knowledge-intensive NLP tasks
RAG models integrate a pre-trained seq2seq generator with a neural retriever over Wikipedia, achieving state-of-the-art results on open-domain QA and generating more factual, diverse text than parametric-only baselines.
Paper: Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks · Patrick Lewis, Ethan Perez, Aleksandra Piktus and 9 others
Classic · 20205 min readRead the article
- AI Agents
- LLM Reasoning
ReAct combines reasoning and acting in language models for better task solving
ReAct prompts large language models to interleave reasoning traces and task-specific actions, improving performance and interpretability on knowledge-intensive QA and interactive decision-making tasks, outperforming baselines with few-shot prompting.
Paper: ReAct: Synergizing Reasoning and Acting in Language Models · Shunyu Yao, Jeffrey Zhao, Dian Yu and 4 others
Classic · 20225 min readRead the article
Browse earlier papers
27 days of papers in October 2026
- Chain-of-thought prompting improves reasoning in large language modelsYou are reading this day
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