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Junlin Zhou

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reacted to Kseniase's post with 👍 7 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
liked a model 13 days ago
NousResearch/DeepHermes-3-Llama-3-8B-Preview
liked a dataset 17 days ago
nvidia/HelpSteer2
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Distributed SFT with trl and DeepSpeed Part 2: Scaling Locally

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Distributed SFT with trl and DeepSpeed Part 1: Starting Locally

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