AGENTIC-SYSTEMSMay 22, 20261 min read7 views

High Quality Embeddings for Horn Logic Reasoning

Researchers have proposed a new method to optimize logical reasoning by improving the quality of embeddings. By utilizing an enhanced triplet loss approach, this method enables neural networks to search for answers faster and more accurately across knowledge bases.

Computer Science > Artificial Intelligence

Title:High Quality Embeddings for Horn Logic Reasoning

View PDF HTML (experimental)Abstract:Neural networks can be trained to rank the choices made by logical reasoners, resulting in more efficient searches for answers. A key step in this process is creating useful embeddings, i.e., numeric representations of logical statements. This paper introduces and evaluates several approaches to creating embeddings that result in better downstream results. We train embeddings using triplet loss, which requires examples consisting of an anchor, a positive example, and a negative example. We introduce three ideas: generating anchors that are more likely to have repeated terms, generating positive and negative examples in a way that ensures a good balance between easy, medium, and hard examples, and periodically emphasizing the hardest examples during training. We conduct several experiments to evaluate this approach, including a comparison of different embeddings across different knowledge bases, in an attempt to identify what characteristics make an embedding well-suited to a particular reasoning task.

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Source: arXiv cs.AI Recent

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