LLMs + Symbols: Picking the Right Meaning Without Labels

Words like "bank" can mean a riverbank or a financial institution. Getting the right meaning—called word sense disambiguation—is hard for AI, especially when we need rich, logic-friendly knowledge for reasoning.

This paper introduces an annotation-free workflow that teams up symbolic language understanding with large language models:

• A symbolic system proposes multiple candidate meanings for a sentence.
• Each candidate is rewritten as a clear natural-language alternative.
• An LLM is asked which alternative fits the context best.
• The chosen meaning is sent back to the symbolic system for deeper inference.

Unlike methods tied to WordNet/FrameNet and hand-labeled data, this approach scales to richer knowledge bases (e.g., OpenCyc) without manual training. In tests against human gold standards, it selects appropriate meanings effectively.

Paper: https://arxiv.org/abs/2511.16577v1

Paper: https://arxiv.org/abs/2511.16577v1

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AI NLP LLM SymbolicAI WordSenseDisambiguation KnowledgeGraphs NLU