[go: up one dir, main page]

Liu et al., 2025 - Google Patents

GRL-Prompt: Towards Prompts Optimization via Graph-Empowered Reinforcement Learning Using LLMs' Feedback

Liu et al., 2025

Document ID
8374546564115539048
Author
Liu Y
Liu T
Zhang T
Xia Y
Wang J
Shen Z
Jin J
Ding Z
Yu F
Publication year
Publication venue
Pacific-Asia Conference on Knowledge Discovery and Data Mining

External Links

Snippet

Large language models (LLMs) have demonstrated impressive success in a wide range of natural language processing tasks due to their extensive general knowledge of the world. The performance of LLMs is heavily dependent on the quality of prompts, while traditional …
Continue reading at link.springer.com (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/3061Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F17/30634Querying
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • G06F17/30386Retrieval requests
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30861Retrieval from the Internet, e.g. browsers
    • G06F17/30864Retrieval from the Internet, e.g. browsers by querying, e.g. search engines or meta-search engines, crawling techniques, push systems
    • G06F17/30867Retrieval from the Internet, e.g. browsers by querying, e.g. search engines or meta-search engines, crawling techniques, push systems with filtering and personalisation
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/20Handling natural language data
    • G06F17/27Automatic analysis, e.g. parsing
    • G06F17/2705Parsing
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/02Knowledge representation
    • G06N5/022Knowledge engineering, knowledge acquisition
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N5/00Computer systems utilising knowledge based models
    • G06N5/04Inference methods or devices
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N7/00Computer systems based on specific mathematical models
    • G06N7/005Probabilistic networks
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/12Computer systems based on biological models using genetic models
    • G06N3/126Genetic algorithms, i.e. information processing using digital simulations of the genetic system
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F15/00Digital computers in general; Data processing equipment in general
    • G06F15/18Digital computers in general; Data processing equipment in general in which a programme is changed according to experience gained by the computer itself during a complete run; Learning machines
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F2216/00Indexing scheme relating to additional aspects of information retrieval not explicitly covered by G06F17/30 and subgroups
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00Administration; Management

Similar Documents

Publication Publication Date Title
Xu et al. Prompting large language models for recommender systems: A comprehensive framework and empirical analysis
Qiu et al. Hierarchical query graph generation for complex question answering over knowledge graph
Cao et al. Relmkg: reasoning with pre-trained language models and knowledge graphs for complex question answering
Liu et al. Modeling the chaotic semantic states of generative artificial intelligence (ai): A quantum mechanics analogy approach
Zhang et al. Unifying paragraph embeddings and neural collaborative filtering for hybrid recommendation
Xue et al. Using MOEA/D for optimizing ontology alignments
CN115511082A (en) Fact verification method based on graph neural network and reinforcement learning
Wang et al. A entity relation extraction model with enhanced position attention in food domain
Chatterjee et al. Fuzzy rough set-based sentence similarity measure and its application to text summarization
Blaauwbroek et al. Learning guided automated reasoning: A brief survey
Shang et al. A survey of large language models on generative graph analytics: Query, learning, and applications
Ma et al. A natural scene recognition learning based on label correlation
Zhang et al. Learning attention embeddings based on memory networks for neural collaborative recommendation
Cao et al. Improving and evaluating complex question answering over knowledge bases by constructing strongly supervised data
Lu et al. Heterogeneous knowledge learning of predictive academic intelligence in transportation
YADAVILLI et al. Explainable sentiment analysis for product reviews using causal graph embeddings
Chen et al. Professional network matters: Connections empower person-job fit
Ali et al. A generic and customizable genetic algorithms-based conceptual model modularization framework
Wang et al. Tag-based self-learning task recommendation for mobile crowdsensing via collaborative multi-expert system
Liu et al. GRL-Prompt: Towards Prompts Optimization via Graph-Empowered Reinforcement Learning Using LLMs’ Feedback
Bai et al. DCRLRec: Dual-domain contrastive reinforcement large language model for recommendation
Wang et al. Explore modeling relation information and direction information in kbqa
Zhao et al. RERG: Reinforced evidence reasoning with graph neural network for table-based fact verification
Knoll Examining the ORKG towards representation of control theoretic knowledge–Preliminary experiences and conclusions
Wang et al. Capturing semantic and syntactic information for link prediction in knowledge graphs