In this article, we propose a new approach to logical reasoning called selection-inference, which leverages the power of large language models to make predictions more interpretable and accurate. Our method exploits these models’ ability to generate text to select the most relevant information for a given task, allowing us to make better decisions with fewer resources.
Background
Logical reasoning is a fundamental aspect of human decision-making, but it can be challenging when dealing with complex tasks or limited information. Traditional approaches rely on hand-crafted rules or heuristics, which can be time-consuming and difficult to apply in practice. With the rise of large language models (LLMs), there is an opportunity to develop more efficient and effective methods for logical reasoning.
Selection-inference
Our proposed method, selection-inference, combines the strengths of LLMs with a novel inference framework. Instead of relying on a single model or heuristic, we use the large language model to generate multiple potential explanations for a given task. These explanations are then ranked using a scoring function, which takes into account both the relevance of each explanation and its coherence with the overall goal. The top-ranked explanation is selected as the final output, providing a more interpretable and accurate prediction.
Evaluation
We evaluate selection-inference on several benchmark tasks, including logical reasoning, natural language inference, and textual entailment. Our results show that our method outperforms existing approaches in terms of both accuracy and efficiency, while also providing more interpretable explanations for the predictions made.
Conclusion
In this article, we presented selection-inference, a novel approach to logical reasoning that leverages the power of large language models. By generating multiple potential explanations and ranking them using a scoring function, our method provides more interpretable and accurate predictions than traditional methods. We demonstrate the effectiveness of selection-inference on several benchmark tasks and show its promise for practical applications. As LLMs continue to improve, we expect selection-inference to play an increasingly important role in enabling more efficient and effective decision-making processes.
