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In the landscape of AI, the ability of LLMs to reason, plan and solve complex problems has become increasingly significant. This novel paper titled “Stream of Search (SoS): Learning to Search in Language” delves into this crucial area, proposing an approach that enhances the reasoning capabilities of LLMs through the concept of search.
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In this post, we will review one of the most recent and effective approaches for improving the quality of instruction-tuning data, known as reflection-tuning.
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In this post we will be looking at the Transformer architecture in detail. You can also find the code for this post on my GitHub Repository.
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Prompt engineering is a discipline focused on crafting efficient prompts for large language models (LLMs). These skills aid in comprehending LLM capabilities and limitations. Researchers apply prompt engineering to enhance LLM performance in various tasks, from QA to arithmetic reasoning. Developers use it to create compelling interfaces for LLMs and other tools.
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In this blog post I tried to introduce top papers in ML (particularly in NLP) and give a brief overview on them.
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In this blog post I tried to introduce top papers in ML (particularly in NLP) and give a brief overview on them.