📚 Regarding Inference and Reasoning Recently, many students have been easily confused about the words' inference 'and' reasoning '. In many Chinese translations, they are often translated as' inference', which are two core and easily confused concepts in the field of big models. The differences between 'reasoning' and 'inference' may vary slightly in different contexts. 1. Inference - Execution process/model call 🌟 Core meaning: The process of a model * * generating output (completion) * * from input (prompt) You call the model once, run forward propagation once, and complete an inference task It is the stage of carrying the reasoning ability of the model 💻 Analogous reality: Just like how you use a calculator to calculate 2+2, the process of "seeing the result by pressing the equal sign" is called inference. 🤖 Example of Large Model Scenario: Input: “Translate ‘Hello’ to French” Output: “Bonjour” The entire generation process from input to output is an inference Reasoning - the process of thinking/the manifestation of ability 🌟 Core meaning: The logical deduction, induction, deduction, hypothesis verification, and other "thinking" processes carried out by a model to obtain the correct answer It is one of the key indicators for measuring the intelligence level of a model Reasoning often requires crossing multiple steps of thinking, and can even be "chain of thought" unfolded 💻 Analogous reality: Just like solving an Olympiad math problem, you need to formulate formulas, analyze conditions, and derive them layer by layer, which is reasoning. 🤖 Example of Large Model Scenario: If all cats can climb trees, and Xiaohei is just one cat, can Xiaohei climb trees Output: "Yes, because Xiaohei is a cat, even cats can climb trees The model here completes deductive reasoning, which is reasoning. 🔎 Summary and Comparison Dimension Reasoning: The essential ability/thinking process, execution/generation process, focus points, logical chain, deduction, correctness, and whether the generation between input and output is measurable. Task evaluation can be designed, such as math problems, logic problems, mainly evaluating speed and accuracy. For example, solving a logic problem, doing mathematical deduction. ChatGPT provides the entire process of technical correlation for the answer Chain of Thought (CoT), Self Consistency accelerator inference optimization (such as TensorRT, quantization) 💡 Summary in layman's terms: Inference is the moment when the model "starts moving" (run it once to give you the answer) Reasoning is the manifestation of whether a model can think or not (the logical process behind the answer) If you are a large-scale model application or system: Inference optimization focuses on reducing costs and accelerating response Reasoning research is concerned with enhancing the intelligence of models and solving more complex problems.