A researcher outlines a method for evaluating the logical consistency of student reports by comparing inputs with inferences using necessary and sufficient conditions. The approach utilizes "reverse-result training" where inference follows the normal path and then continues from the result back to the beginning, allowing the model to calculate similarity scores between the original text and its reconstruction.
- Normal training and inferencing are treated as a necessary condition (A->B).
- Reverse training and inferencing act as a sufficient condition (B->A) by tracing back from the result to the start.
- Similarity thresholds determine logical quality: >0.8 is fine, 0.7-0.5 indicates a skip, and <0.5 signals an error.
The author suggests this mechanism could be integrated into or replace parts of LLM architectures to reduce hallucination.