Repository for the paper "The CoT Collection: Improving Zero-shot and Few-shot Learning of Language Models via Chain-of-Thought Fine-Tuning", including 1.84M CoT rationales extracted across 1,060 tasks"
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code was clean, organized, and logical . Each decision made in their code made sense, and when something didn’t, it was documented well within the code.
A good way to write clean code is to follow principles, like the SOLID principles. Though they were initially designed with OOP (object-oriented programming) in mind, they are extensible to general... See more
I really think that we could be entering a third epoch of computing. The microchip brought the marginal cost of compute to 0. The internet brought the marginal cost of distribution to 0. These large models actually bring the marginal cost of creation to 0.
Not just the cost of creation but also the cost of intelligence.
Google Deepmind used similar idea to make LLMs faster in Accelerating Large Language Model Decoding with Speculative Sampling. Their algorithm uses a smaller draft model to make initial guesses and a larger primary model to validate them. If the draft often guesses right, operations become faster, reducing latency.
Thanks to the cognitive architecture design, goal-oriented behavior is at the core of our LLM agents. When presented with a goal, they employ a planning and execution process to achieve it. If the goal can be accomplished using atomic game actions, we generate a plan that outlines how to solve the objective. For longer-term goals, we decompose the... See more