ProSLM : A Prolog Synergized Language Model for explainable Domain Specific Knowledge Based Question Answering

• URL: http://arxiv.org/abs/2409.11589v1
• Date: Tue, 17 Sep 2024 22:34:33 GMT
• Title: ProSLM : A Prolog Synergized Language Model for explainable Domain Specific Knowledge Based Question Answering
• Authors: Priyesh Vakharia, Abigail Kufeldt, Max Meyers, Ian Lane, Leilani Gilpin,
• Abstract summary: Neurosymbolic approaches can add robustness to opaque neural systems by incorporating explainable symbolic representations. We propose systemname, a novel neurosymbolic framework, to improve robustness and reliability of large language models. Our work opens a new area of neurosymbolic generative AI text validation and user personalization.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Neurosymbolic approaches can add robustness to opaque neural systems by incorporating explainable symbolic representations. However, previous approaches have not used formal logic to contextualize queries to and validate outputs of large language models (LLMs). We propose \systemname{}, a novel neurosymbolic framework, to improve the robustness and reliability of LLMs in question-answering tasks. We provide \systemname{} with a domain-specific knowledge base, a logical reasoning system, and an integration to an existing LLM. This framework has two capabilities (1) context gathering: generating explainable and relevant context for a given query, and (2) validation: confirming and validating the factual accuracy of a statement in accordance with a knowledge base (KB). Our work opens a new area of neurosymbolic generative AI text validation and user personalization.

Related papers

• Neurosymbolic AI approach to Attribution in Large Language Models [5.3454230926797734]
  Neurosymbolic AI (NesyAI) combines the strengths of neural networks with structured symbolic reasoning. This paper explores how NesyAI frameworks can enhance existing attribution models, offering more reliable, interpretable, and adaptable systems.
  arXiv  Detail & Related papers  (2024-09-30T02:20:36Z)
• Proof of Thought : Neurosymbolic Program Synthesis allows Robust and Interpretable Reasoning [1.3003982724617653]
  Large Language Models (LLMs) have revolutionized natural language processing, yet they struggle with inconsistent reasoning. This research introduces Proof of Thought, a framework that enhances the reliability and transparency of LLM outputs. Key contributions include a robust type system with sort management for enhanced logical integrity, explicit representation of rules for clear distinction between factual and inferential knowledge.
  arXiv  Detail & Related papers  (2024-09-25T18:35:45Z)
• Prompt2DeModel: Declarative Neuro-Symbolic Modeling with Natural Language [18.00674366843745]
  This paper presents a pipeline for crafting domain knowledge for complex neuro-symbolic models through natural language prompts. Our proposed pipeline utilizes techniques like dynamic in-context demonstration retrieval, model refinement based on feedback from a symbolic visualization, and user interaction. This approach empowers domain experts, even those not well-versed in ML/AI, to formally declare their knowledge to be incorporated in customized neural models.
  arXiv  Detail & Related papers  (2024-07-30T03:10:30Z)
• On the verification of Embeddings using Hybrid Markov Logic [2.113770213797994]
  We propose a framework to verify complex properties of a learned representation. We present an approach to learn parameters for the properties within this framework. We illustrate verification in Graph Neural Networks, Deep Knowledge Tracing and Intelligent Tutoring Systems.
  arXiv  Detail & Related papers  (2023-12-13T17:04:09Z)
• DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain Question Answering over Knowledge Base and Text [73.68051228972024]
  Large Language Models (LLMs) have exhibited impressive generation capabilities, but they suffer from hallucinations when relying on their internal knowledge. Retrieval-augmented LLMs have emerged as a potential solution to ground LLMs in external knowledge.
  arXiv  Detail & Related papers  (2023-10-31T04:37:57Z)
• Towards Verifiable Generation: A Benchmark for Knowledge-aware Language Model Attribution [48.86322922826514]
  This paper defines a new task of Knowledge-aware Language Model Attribution (KaLMA) First, we extend attribution source from unstructured texts to Knowledge Graph (KG), whose rich structures benefit both the attribution performance and working scenarios. Second, we propose a new Conscious Incompetence" setting considering the incomplete knowledge repository. Third, we propose a comprehensive automatic evaluation metric encompassing text quality, citation quality, and text citation alignment.
  arXiv  Detail & Related papers  (2023-10-09T11:45:59Z)
• The Whole Truth and Nothing But the Truth: Faithful and Controllable Dialogue Response Generation with Dataflow Transduction and Constrained Decoding [65.34601470417967]
  We describe a hybrid architecture for dialogue response generation that combines the strengths of neural language modeling and rule-based generation. Our experiments show that this system outperforms both rule-based and learned approaches in human evaluations of fluency, relevance, and truthfulness.
  arXiv  Detail & Related papers  (2022-09-16T09:00:49Z)
• elBERto: Self-supervised Commonsense Learning for Question Answering [131.51059870970616]
  We propose a Self-supervised Bidirectional Representation Learning of Commonsense framework, which is compatible with off-the-shelf QA model architectures. The framework comprises five self-supervised tasks to force the model to fully exploit the additional training signals from contexts containing rich commonsense. elBERto achieves substantial improvements on out-of-paragraph and no-effect questions where simple lexical similarity comparison does not help.
  arXiv  Detail & Related papers  (2022-03-17T16:23:45Z)
• Question Answering over Knowledge Bases by Leveraging Semantic Parsing and Neuro-Symbolic Reasoning [73.00049753292316]
  We propose a semantic parsing and reasoning-based Neuro-Symbolic Question Answering(NSQA) system. NSQA achieves state-of-the-art performance on QALD-9 and LC-QuAD 1.0.
  arXiv  Detail & Related papers  (2020-12-03T05:17:55Z)
• Leap-Of-Thought: Teaching Pre-Trained Models to Systematically Reason Over Implicit Knowledge [96.92252296244233]
  Large pre-trained language models (LMs) acquire some reasoning capacity, but this ability is difficult to control. We show that LMs can be trained to reliably perform systematic reasoning combining both implicit, pre-trained knowledge and explicit natural language statements. Our work paves a path towards open-domain systems that constantly improve by interacting with users who can instantly correct a model by adding simple natural language statements.
  arXiv  Detail & Related papers  (2020-06-11T17:02:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.