Summary of Trace-cs: a Synergistic Approach to Explainable Course Scheduling Using Llms and Logic, by Stylianos Loukas Vasileiou et al.
TRACE-CS: A Synergistic Approach to Explainable Course Scheduling Using LLMs and Logic
by Stylianos Loukas Vasileiou, William Yeoh
First submitted to arxiv on: 5 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The novel hybrid system TRACE-cs combines symbolic reasoning and large language models (LLMs) to address contrastive queries in scheduling problems. This system leverages SAT solving techniques to encode scheduling constraints, generating explanations for user queries, while utilizing an LLM to process the user queries into logical clauses and refine the explanations generated by the symbolic solver into natural language sentences. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TRACE-cs is a way that computers can understand and explain how they make decisions. It helps with scheduling problems, like planning a schedule for a school or company. The system uses two main parts: one that uses logic to solve the problem, and another that uses big language models (like those used in chatbots) to make sure the explanations are clear and natural-sounding. |
