Just run the LLM five times and choose the most common answer

Self-Consistency Sampling: A Secret Trick for Improving AI Performance

In the world of artificial intelligence (AI), there are numerous techniques and strategies used to enhance the performance of AI models. One such technique that is not widely known or utilized is self-consistency sampling. While it may sound complicated, self-consistency sampling is a powerful tool that can greatly benefit AI production.

Self-consistency sampling involves generating multiple responses to a given question or prompt and then choosing the most suitable response based on various criteria. This can be done by selecting the summary or aggregate of the responses, choosing the most common response, or even using evaluation metrics to determine the best response.

The concept of self-consistency sampling is not new and is well-known in the academic world. However, its application in AI production is not as prevalent as it should be. By leveraging the non-deterministic nature of language models, self-consistency sampling allows for the generation of multiple responses, increasing the chances of obtaining the correct answer in aggregate.

To better understand self-consistency sampling, let's consider a canonical example. Instead of generating a single response to a question, this technique generates three responses. In this example, two of the three responses are correct, while one is incorrect. By selecting the most consistent responses, we can arrive at the correct answer, even if individual responses may vary.

Implementing self-consistency sampling can be made easier using asynchronous OpenAI, which allows for quicker execution of multiple tasks simultaneously. By running multiple samples asynchronously, we can reduce latency and obtain more accurate results. This is particularly useful when running three or five samples, as running them sequentially would significantly increase the processing time.

To demonstrate the use of self-consistency sampling, a code snippet utilizing asynchronous calls is provided in the transcript. The code generates multiple responses and then gathers them to find the most common answer. Even in the presence of errors or inconsistencies, the right answer can still be obtained through this technique.

Moreover, self-consistency sampling allows for flexibility in adjusting the number of samples generated. By increasing the number of samples, the likelihood of obtaining a successful outcome also increases. This trade-off between quality and cost can be advantageous, as generating more samples does not significantly impact latency but improves the overall performance of the AI model.

Aside from selecting the most common answer, self-consistency sampling can also incorporate evaluation metrics to assess the correctness of the responses. This adds an additional layer of validation to ensure the accuracy of the generated output. Additionally