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Volkan şimşir

VolkanSimsir
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published a model 2 days ago
VolkanSimsir/gemma-3-4b-it-finetuned
reacted to Kseniase's post with 🔥 24 days ago
9 types of "Chain-of-..." approaches: Chain-of-Thought (CoT) prompting enhances reasoning in AI models by breaking down complex problems into step-by-step logical sequences. It continues proving its effectiveness, especially in top-performing reasoning models. However, there are other similar methods, that expand CoT and can be used for different purposes. Here are 9 of them: 1. Chain-of-Action-Thought (COAT) -> https://huggingface.co/papers/2502.02508 Helps model decide when to keep thinking, double-check their work, or try a different approach, using special guiding tokens. 2. Chain of Draft (CoD) -> https://huggingface.co/papers/2502.18600 It helps model generate short but meaningful reasoning steps, cutting costs and making processing faster 3. Chain-of-Agents -> https://huggingface.co/papers/2406.02818 Uses multi-agent collaboration: Worker agents process text parts in a structured chain, and manager agent summarizes the results 4. Chain-of-RAG ->https://huggingface.co/papers/2501.14342 Creates retrieval chains, instead of retrieving all info at once. It can dynamically adjust its search process and its parameters like step number 5. Chain-of-Shot Prompting (CoS) -> https://huggingface.co/papers/2502.06428 Helps models pick frames crucial for understanding a video, using a binary video summary and video co-reasoning module. 6. Chain of Hindsight (CoH) -> https://huggingface.co/papers/2302.02676 Converts all feedback into sequences to fine-tune the model and refine outputs 7. Chain-of-Note (CoN) -> https://huggingface.co/papers/2311.09210 Generates sequential reading notes for each retrieved document to assess relevance before integrating info into the final answer 8. Chain of Diagnosis (CoD) -> https://huggingface.co/papers/2407.13301 Transforms the diagnostic process into a diagnostic chain 9. Chain(s)-of-Knowledge -> https://www.turingpost.com/p/cok Enhance LLMs by dynamically pulling in external knowledge to improve accuracy and reduce errors
reacted to Kseniase's post with 👍 about 1 month ago
8 Free Sources about AI Agents: Agents seem to be everywhere and this collection is for a deep dive into the theory and practice: 1. "Agents" Google's whitepaper by Julia Wiesinger, Patrick Marlow and Vladimir Vuskovic -> https://www.kaggle.com/whitepaper-agents Covers agents, their functions, tool use and how they differ from models 2. "Agents in the Long Game of AI. Computational Cognitive Modeling for Trustworthy, Hybrid AI" book by Marjorie McShane, Sergei Nirenburg, and Jesse English -> https://direct.mit.edu/books/oa-monograph/5833/Agents-in-the-Long-Game-of-AIComputational Explores building AI agents, using Hybrid AI, that combines ML with knowledge-based reasoning 3. "AI Engineer Summit 2025: Agent Engineering" 8-hour video -> https://www.youtube.com/watch?v=D7BzTxVVMuw Experts' talks that share insights on the freshest Agent Engineering advancements, such as Google Deep Research, scaling tips and more 4. AI Agents Course from Hugging Face -> https://huggingface.co/learn/agents-course/en/unit0/introduction Agents' theory and practice to learn how to build them using top libraries and tools 5. "Artificial Intelligence: Foundations of Computational Agents", 3rd Edition, book by David L. Poole and Alan K. Mackworth -> https://artint.info/3e/html/ArtInt3e.html Agents' architectures, how they learn, reason, plan and act with certainty and uncertainty 6. "Intelligent Agents: Theory and Practice" book by Michael Wooldridge -> https://www.cs.ox.ac.uk/people/michael.wooldridge/pubs/ker95/ker95-html.html A fascinating option to dive into how agents were seen in 1995 and explore their theory, architectures and agent languages 7. The Turing Post articles "AI Agents and Agentic Workflows" on Hugging Face -> https://huggingface.co/Kseniase We explore agentic workflows in detail and agents' building blocks, such as memory and knowledge 8. Our collection "8 Free Sources to Master Building AI Agents" -> https://www.turingpost.com/p/building-ai-agents-sources
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