hors test this concept using different embedding methods, including off-the-shelf dense embeddings, fine-tuned embeddings from GPT3, and sparse embeddings from BM25. They evaluate these methods both intrinsically, to see if the property of deductive additivity holds, and extrinsically, using them to assist planning in natural language proof generation.

The implications of this research are significant for the field of NLP. If successful, this approach could provide a scalable method for planning in natural