OPEA/QwQ-32B-Preview-int4-sym-mixed-inc
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How can I make my RAG application generate real-time responses? Up until now, I have been using Groq for fast LLM generation and the Gradio Live function. I am looking for a better solution that can help me build a real-time application without any delay.
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kingabzpro/Real-Time-RAG
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Google's Chain-of-Thought (CoT) is one of the most effective ways to improve LLMs' reasoning.
Researchers have now developed a novel approach called Strategic Chain-of-Thought (SCoT) to enhance the reasoning capabilities of large language models even further.
š§ SCoT uses a two-stage process within a single prompt:
- Strategy Elicitation: The model first identifies and determines an effective problem-solving strategy for the given task. This becomes the strategic knowledge that guides the reasoning process.
- Strategy Application: The model then applies the identified strategic knowledge to solve the problem and generate the final answer.
Essentially, SCoT integrates strategic knowledge to guide reasoning without relying on external knowledge sources or multiple queries.
According to the research, SCoT showed significant improvements over standard CoT across various datasets, including a 21.05% increase on the GSM8K math dataset and a 24.13% increase on the Tracking_Objects spatial reasoning task.
Changes in the Prompt Structure:
The SCoT prompt typically consists of five components:
- Role: Defines the expert role the model should assume.
- Workflow: Outlines the steps for strategy identification and application.
- Rules: Specifies guidelines for generating answers.
- Initialization: Sets up the task.
- Task Input: Provides the specific problem to solve.
Strategy Generation:
The model is prompted to generate strategic knowledge relevant to the problem domain. For example, in mathematics, it might favor elegant solutions like using arithmetic series formulas over brute-force calculations.
Guided Reasoning:
Using the elicited strategy, the model then generates a chain-of-thought reasoning path. This approach aims to produce more stable and higher-quality outputs compared to standard chain-of-thought methods.
Read the full paper: https://arxiv.org/abs/2409.03271
Researchers have now developed a novel approach called Strategic Chain-of-Thought (SCoT) to enhance the reasoning capabilities of large language models even further.
š§ SCoT uses a two-stage process within a single prompt:
- Strategy Elicitation: The model first identifies and determines an effective problem-solving strategy for the given task. This becomes the strategic knowledge that guides the reasoning process.
- Strategy Application: The model then applies the identified strategic knowledge to solve the problem and generate the final answer.
Essentially, SCoT integrates strategic knowledge to guide reasoning without relying on external knowledge sources or multiple queries.
According to the research, SCoT showed significant improvements over standard CoT across various datasets, including a 21.05% increase on the GSM8K math dataset and a 24.13% increase on the Tracking_Objects spatial reasoning task.
Changes in the Prompt Structure:
The SCoT prompt typically consists of five components:
- Role: Defines the expert role the model should assume.
- Workflow: Outlines the steps for strategy identification and application.
- Rules: Specifies guidelines for generating answers.
- Initialization: Sets up the task.
- Task Input: Provides the specific problem to solve.
Strategy Generation:
The model is prompted to generate strategic knowledge relevant to the problem domain. For example, in mathematics, it might favor elegant solutions like using arithmetic series formulas over brute-force calculations.
Guided Reasoning:
Using the elicited strategy, the model then generates a chain-of-thought reasoning path. This approach aims to produce more stable and higher-quality outputs compared to standard chain-of-thought methods.
Read the full paper: https://arxiv.org/abs/2409.03271
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