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As language models become increasingly common, it becomes crucial to employ a broad set of strategies and tools in order to fully unlock their potential. Foremost among these strategies is prompt engineering, which involves the careful selection and arrangement of words within a prompt or query in order to guide the model towards producing theContinue reading "The LLM Triad: Tune, Prompt, Reward"
Open-Source LLM Explained: A Beginner's Journey Through Large Language Models, by ByFintech @ AI4Finance Foundation
Retrieval-Augmented Generation for Large Language Models A Survey, PDF, Information Retrieval
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Edge 377: LLM Reasoning with Reinforced Fine-Tuning
Demystifying Large Language Models for Everyone: Fine-Tuning Your Own LLM. Part 1/3, by Jair Neto
Gradient Flow
Finetuning an LLM: RLHF and alternatives (Part II)
Retrieval-Augmented Generation for Large Language Models A Survey, PDF, Information Retrieval
Reinforcement Learning from Human Feedback (RLHF), by kanika adik
Understanding RLHF for LLMs
The LLM Triad: Tune, Prompt, Reward - Gradient Flow
Some Core Principles of Large Language Model (LLM) Tuning, by Subrata Goswami
Gradient Flow
Paper page - Directly Fine-Tuning Diffusion Models on Differentiable Rewards
Tuning Recurrent Neural Networks with Reinforcement Learning