Summary of Proslm : a Prolog Synergized Language Model For Explainable Domain Specific Knowledge Based Question Answering, by Priyesh Vakharia et al.
ProSLM : A Prolog Synergized Language Model for explainable Domain Specific Knowledge Based Question Answering
by Priyesh Vakharia, Abigail Kufeldt, Max Meyers, Ian Lane, Leilani Gilpin
First submitted to arxiv on: 17 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 proposed system, , is a novel neurosymbolic framework that improves the robustness and reliability of large language models (LLMs) in question-answering tasks. This framework integrates formal logic to contextualize queries and validate outputs of LLMs. It consists of three components: a domain-specific knowledge base, a logical reasoning system, and an integration with an existing LLM. The framework has two capabilities: context gathering, which generates explainable and relevant context for a given query, and validation, which confirms the factual accuracy of a statement based on a knowledge base (KB). This work opens up a new area of neurosymbolic generative AI text validation and user personalization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A team of researchers has created a new way to use artificial intelligence (AI) to answer questions. They made a special system called that helps large language models, which are like super smart computers, give more accurate answers. This system uses rules from math and logic to make sure the answers are correct. It also looks at what you asked and gives you extra information to help you understand the answer better. This new way of using AI could be very helpful for people who want to get information quickly and accurately. |
Keywords
» Artificial intelligence » Knowledge base » Question answering
