How to Use LLMs for Powerful Automatic Evaluations
discuss how you can perform automatic evaluations using LLM as a judge. LLMs are widely used today for a variety ...
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Coconut: A Framework for Latent Reasoning in LLMs
Paper link: https://arxiv.org/abs/2412.06769 Released: 9th of December 2024 Figure 1. The two reasoning modes of Coconut. In Language Mode (left), ...
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Demystifying Cosine Similarity | Towards Data Science
is a commonly used metric for operationalizing tasks such as semantic search and document comparison in the field of natural ...
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Generating Structured Outputs from LLMs
interface for interacting with LLMs is through the classic chat UI found in ChatGPT, Gemini, or DeepSeek. The interface is ...
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Finding Golden Examples: A Smarter Approach to In-Context Learning
Context using Large Language Models (LLMs), In-Context Learning (ICL), where input and output are provided to LLMs to learn from ...
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Talk to my Agent | Towards Data Science
the past several months, I’ve had the opportunity to immerse myself in the task of adapting APIs and backend systems ...
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How I Fine-Tuned Granite-Vision 2B to Beat a 90B Model — Insights and Lessons Learned
or vision-language models is a powerful technique that unlocks their potential on specialized tasks. However, despite their effectiveness, these approaches ...
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How To Significantly Enhance LLMs by Leveraging Context Engineering
is the science of providing LLMs with the correct context to maximize performance. When you work with LLMs, you typically ...
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Your 1M+ Context Window LLM Is Less Powerful Than You Think
are now able to handle vast inputs — their context windows range between 200K (Claude) and 2M tokens (Gemini 1.5 Pro). That’s ...
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Do You Really Need a Foundation Model?
are everywhere — but are they always the right choice? In today’s AI world, it seems like everyone wants to use foundation ...
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