TRACE-CS: A Synergistic Approach to Explainable Course Scheduling Using LLMs and Logic
<|reference_start|>TRACE-CS: A Synergistic Approach to Explainable Course Scheduling Using LLMs and Logic: We present TRACE-cs, a novel hybrid system that combines symbolic reasoning with large language models (LLMs) to address contrastive queries in scheduling problems. TRACE-cs leverages SAT solving techniques to encode scheduling constraints and generate explanations for user queries, while utilizing an LLM to process the user queries into logical clauses as well as refine the explanations generated by the symbolic solver to natural language sentences. By integrating these components, our approach demonstrates the potential of combining symbolic methods with LLMs to create explainable AI agents with correctness guarantees.<|reference_end|>
@article{vasileiou2024trace-cs:,
    title={TRACE-CS: A Synergistic Approach to Explainable Course Scheduling Using
  LLMs and Logic},
    author={Stylianos Loukas Vasileiou, William Yeoh},
    journal={arXiv preprint arXiv:2409.03671},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.03671},
    primaryClass={cs.AI}
}