When Deductive Reasoning Fails: Contextual Ambiguities in AI Models | HackerNoon

While we have demonstrated the effectiveness of Natural Program-based deductive reasoning verification to enhance trustworthiness and interpretability, our approach has limitations. A common source of failures involves ambiguous terms, such as 'pennies,' which could mean either a type of coin or a unit of currency. In our example, the ground truth interprets 'pennies' as coin, while ChatGPT sees it as currency. This showcases a notable limitation in our deductive verification process, reflecting the challenges posed by contextual ambiguities that arise in real-world scenarios.

The failure case highlights our approach's inability to resolve misinterpretations due to ambiguous language. Such issues pose significant challenges in the field of Natural Language Processing, where the context heavily influences meaning. Understanding how various interpretations can affect reasoning processes is crucial for improving the technology and creating models that not only generate accurate answers but also meaningfully engage with the nuances of language.

Acknowledging these limitations is vital. It not only helps refine our methodology but also prepares future researchers for the intricacies involved in language processing. Strategies to mitigate these ambiguities must be prioritized to enhance the effectiveness of deductive reasoning approaches in AI. By focusing on the push to improve contextual understanding, AI systems can become more adept at handling the nuances of human language.

Read at Hackernoon