Skip to content 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), ...
Read more 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 ...
Read more 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 ...
Read more 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 ...
Read more 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 ...
Read more 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 ...
Read more 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 ...
Read more 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 ...
Read more 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 ...
Read more Are You Being Unfair to LLMs?
hype surrounding AI, some ill-informed ideas about the nature of LLM intelligence are floating around, and I’d like to address ...
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