How Do I Improve the Accuracy of an LLM in Complex Inference Tasks Using Prompts

You can use the chain-of-thought (CoT) method to improve the accuracy of an LLM in complex inference tasks.

CoT is a method of improving the performance of an LLM in complex tasks through step-by-step reasoning. By guiding the model to think through the problem, it can achieve higher accuracy in inference tasks, especially those involving multi-step reasoning and complex logical relationships.

You can take the following steps:

1. Provide related examples. Include similar examples in the prompt to help the model learn the problem-solving patterns and concepts. With these examples, the model can understand how to gradually derive conclusions through different reasoning steps.

   Take a math problem as an example. By demonstrating the entire process—from understanding the problem, applying the relevant formula, to giving the final solution—you help the model grasp the logical steps involved in problem solving.

2. Guide the model to analyze. If there is no direct example or the existing example is not applicable, you can guide the model to perform detailed analysis before providing the answer. This means that the prompt explicitly requires the model to analyze all aspects of the problem step by step, helping the model consume more computing resources for comprehensive reasoning. In this way, the model can draw a more accurate conclusion after multiple reasoning steps, instead of jumping to the final answer, reducing the possibility of over-simplification or jumping-to-conclusion reasoning.
3. Combine step-by-step reasoning and feedback. The model can check and correct its thinking process after each step.

   For example, for a complex logical inference question, ask the model to provide intermediate conclusions and reasoning processes after completing each small step of reasoning. This improves the problem solving accuracy of the model, and enhances its understanding and self-correction capabilities.

By using the chain-of-thought approach in the prompt, providing relevant examples, or guiding the model to analyze problems step by step, the accuracy of a foundation model in complex inference tasks can be effectively improved. This method helps the model better understand the problem and enhances its inference capability, especially when processing tasks that require multi-step inference.