Summary of Dana: Domain-aware Neurosymbolic Agents For Consistency and Accuracy, by Vinh Luong et al.
DANA: Domain-Aware Neurosymbolic Agents for Consistency and Accuracy
by Vinh Luong, Sang Dinh, Shruti Raghavan, William Nguyen, Zooey Nguyen, Quynh Le, Hung Vo, Kentaro Maegaito, Loc Nguyen, Thao Nguyen, Anh Hai Ha, Christopher Nguyen
First submitted to arxiv on: 27 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 paper introduces DANA (Domain-Aware Neurosymbolic Agent), an architecture that addresses inconsistencies and inaccuracies in Large Language Models (LLMs) by integrating domain-specific knowledge with neurosymbolic approaches. By analyzing current AI architectures through a neurosymbolic lens, the authors highlight how their reliance on probabilistic inference contributes to inconsistent outputs. DANA captures and applies domain expertise in both natural-language and symbolic forms, enabling more deterministic and reliable problem-solving behaviors. The implementation of DANA using Hierarchical Task Plans (HTPs) in OpenSSA framework achieves over 90% accuracy on the FinanceBench financial-analysis benchmark, outperforming current LLM-based systems in consistency and accuracy. Applications of DANA in physical industries like semiconductors show its potential in tackling complex real-world problems that require reliability and precision. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary DANA is a new way to make Large Language Models (LLMs) better at solving problems. Right now, these models can be really good at some things, but they’re not always consistent or accurate. DANA fixes this by adding special knowledge about a specific area (like finance) and using symbols to help it understand what’s going on. This makes it much more reliable and accurate. In fact, when tested with financial data, DANA was able to get over 90% of answers correct, which is way better than other models. |
Keywords
» Artificial intelligence » Inference » Precision
