Decoupling Formal and Natural Language Token Embeddings in Fine-Tuned Models for Improved Ontology Integration

Tracking #: 887-1897

Flag : Reject (Pre-Screening)

Authors: 

Richard Thompson
Adam Pease
Angelos Toutsios
Mathias Kolsch

Submission Type: 

Article in Special Issue (note in cover letter)

Full PDF Version: 

Cover Letter: 

This submission is an extended version of a paper published in the proceedings of "NeSy 2025 – the International Conference on Neurosymbolic Learning and Reasoning" titled ”Grounding Terms from an Ontology for use in Autoformalization: Tokenization is All You Need.” This submission is intended for a Special Issue on NeSy 2025. Notable changes from the previous version of the paper include: * Added table with examples of hallucinated terms produced by fine-tuned LLM. * Addressed potential alternative solutions, such as Retrieval-augmented generation (RAG). * Discussed importance of work to neuro-symbolic reasoning. * Added section outlining evaluation metrics. * Provided discussion and Figure on how synthetic training data was generated. * Re-ordered sections, placing related work after the background section. * Re-titled paper to be more descriptive of content.

Approve Decision: 

Approved

Tags: 

  • Reviewed

Decision: 

Reject (Pre-Screening)