Summary of Dspy-based Neural-symbolic Pipeline to Enhance Spatial Reasoning in Llms, by Rong Wang et al.
Dspy-based Neural-Symbolic Pipeline to Enhance Spatial Reasoning in LLMs
by Rong Wang, Kun Sun, Jonas Kuhn
First submitted to arxiv on: 27 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 neural-symbolic framework presented in this paper enhances Large Language Models’ (LLMs) spatial reasoning abilities through iterative feedback between LLMs and Answer Set Programming (ASP). The authors evaluate their approach on two benchmark datasets, StepGame and SparQA, using three distinct strategies: direct prompting, Facts+Rules prompting, and a DSPy-based LLM+ASP pipeline with iterative refinement. The results show that the LLM+ASP pipeline significantly outperforms baseline methods, achieving an average 82% accuracy on StepGame and 69% on SparQA. The success stems from three key innovations: modular pipeline, iterative feedback mechanism, and robust error handling. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers become better at understanding spatial information. It uses a new way to combine two types of artificial intelligence (AI): large language models and answer set programming. The authors test their approach on two sets of problems and show that it does much better than the old ways of doing things. They also provide insights into how different AI systems work and what makes them good at certain tasks. |
Keywords
» Artificial intelligence » Prompting
