6 Research Papers that are the Pillars of AI

They are the reason why AI systems today understand language, solve problems, reason step by step, and scale so effectively. Every AI Engineer should read.

𝟏. 𝐀𝐭𝐭𝐞𝐧𝐭𝐢𝐨𝐧 𝐈𝐬 𝐀𝐥𝐥 𝐘𝐨𝐮 𝐍𝐞𝐞𝐝 (𝟐𝟎𝟏𝟕)

• Introduced the Transformer architecture, replacing older RNN/CNN models.
• Allowed models to focus on the most relevant parts of data through the “attention” mechanism.
• Became the backbone of almost every modern LLM, including GPT, Gemini, and Claude.
• Link: https://lnkd.in/ejMS4ne6

𝟐. 𝐁𝐄𝐑𝐓: 𝐏𝐫𝐞-𝐭𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐨𝐟 𝐃𝐞𝐞𝐩 𝐁𝐢𝐝𝐢𝐫𝐞𝐜𝐭𝐢𝐨𝐧𝐚𝐥 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐞𝐫𝐬 (𝟐𝟎𝟏𝟗)

• Introduced masked language modeling predicting missing words during pretraining.
• Enabled deeper contextual understanding of language.
• Significantly improved performance on tasks like search, classification, and question answering.
• Link: https://lnkd.in/eWKCcPJH

𝟑. 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥𝐬 𝐀𝐫𝐞 𝐅𝐞𝐰-𝐒𝐡𝐨𝐭 𝐋𝐞𝐚𝐫𝐧𝐞𝐫𝐬 (𝐆𝐏𝐓-𝟑, 𝟐𝟎𝟐𝟎)

• Proved that scaling up model size unlocks emergent abilities.
• Showed that models can perform new tasks with just a few examples, without retraining.
• Shifted AI from narrow, task-specific tools to powerful general-purpose systems.
• Link: https://lnkd.in/eW2NsDdh

𝟒. 𝐒𝐜𝐚𝐥𝐢𝐧𝐠 𝐋𝐚𝐰𝐬 𝐟𝐨𝐫 𝐍𝐞𝐮𝐫𝐚𝐥 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥𝐬 (𝟐𝟎𝟐𝟎)

• Demonstrated how performance scales predictably with model size, data, and compute.
• Provided a roadmap for building and scaling frontier models.
• Influenced how today’s largest LLMs are planned and developed.
• Link: https://lnkd.in/ee-KkEjN

𝟓. 𝐂𝐡𝐚𝐢𝐧-𝐨𝐟-𝐓𝐡𝐨𝐮𝐠𝐡𝐭 𝐏𝐫𝐨𝐦𝐩𝐭𝐢𝐧𝐠 𝐄𝐥𝐢𝐜𝐢𝐭𝐬 𝐑𝐞𝐚𝐬𝐨𝐧𝐢𝐧𝐠 𝐢𝐧 𝐋𝐚𝐫𝐠𝐞 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥𝐬 (𝟐𝟎𝟐𝟐)

• Showed that prompting models to “think step by step” greatly enhances reasoning.
• Enabled better performance on complex tasks requiring logical steps.
• Became a core technique in prompting, reasoning pipelines, and agentic AI systems.
• Link: https://lnkd.in/ejsu_mqZ

𝟔. 𝐋𝐋𝐚𝐌𝐀: 𝐎𝐩𝐞𝐧 𝐚𝐧𝐝 𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐌𝐨𝐝𝐞𝐥𝐬 (𝟐𝟎𝟐𝟑)

• Proved that strong LLMs don’t require massive compute resources.
• Delivered efficient and open-source models that perform exceptionally well.
• Sparked the open-source LLM revolution and democratized access to advanced AI.
• Link: https://lnkd.in/eppy7hFu

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