This diagram illustrates the difference between static and dynamic prompt generation in the field of computer science. The examples in the image are as follows: 1. Static Prompt Example (Multi-hop Question Answering Task Scenario): For the multi-hop knowledge question answering task "Briefly describe the core differences and historical impact of the Tang Dynasty's imperial examination system compared to the Song Dynasty's imperial examination system," the static prompt uses a manually preset fixed template. The content is "Please answer the following question: {question}. Please elaborate in points and use concise language." This prompt template does not differentiate between the depth of historical knowledge involved in the question or the complexity of the reasoning steps (this question requires at least two steps of reasoning: first, sort out the core content of the Tang/Song Dynasty imperial examination system, then compare the differences and analyze the impact). It also does not adapt to the capability level of the large language model used (e.g., it does not supplement basic concept explanations for basic-level models, nor does it add in-depth analysis guidance for advanced models). It always acts on the model with a unified expression, which can easily lead to basic models being unable to complete reasoning due to a lack of conceptual groundwork, and advanced models outputting superficial content due to insufficient guidance. 2. Dynamic Prompt Example (Same Multi-hop Question Answering Task Scenario): For the same question above, the dynamic prompt generation process needs to go through four adaptive stages: "task analysis - knowledge retrieval - model adaptation - prompt generation." First, the task analysis module identifies that the question belongs to a multi-hop reasoning task in the field of history, requiring a 2-3 step logic chain. Second, the RAG mechanism retrieves core knowledge points of the Tang Dynasty's imperial examination system (such as the status of the Jinshi subject and the non-implementation of the anonymity system) and the Song Dynasty's imperial examination system (such as the implementation of the anonymity transcription system and the institutionalization of the palace examination). Then, combined with the characteristics of the model used (assuming it is an intermediate-level model), it is determined that basic concept transitions need to be supplemented, but oversimplification is not necessary. Finally, a dynamic prompt is generated: "Please complete the question analysis based on the following historical knowledge, and answer in three steps of reasoning: 1. First, clarify the core characteristics of the Tang Dynasty's imperial examination system (reference: Jinshi subject as the core, no anonymity system, small number of admissions); 2. Then, sort out the key changes in the Song Dynasty's imperial examination system (reference: implementation of the anonymity transcription system, normalization of the palace examination, expansion of the scale of admissions); 3. Compare the core differences between the two and elaborate on their historical impact on social class mobility and the construction of the bureaucratic system at that time. Question: Briefly describe the core differences and historical impact of the Tang Dynasty's imperial examination system compared to the Song Dynasty's imperial examination system." This prompt can dynamically adjust the expression and reasoning guidance logic according to the complexity of the question, the retrieved knowledge details, and the model's capabilities.
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