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Found 9 papers, 2 papers with code
Benchmarking Large Language Models in Complex Question Answering Attribution using Knowledge Graphs
The attribution of question answering is to provide citations for supporting generated statements, and has attracted wide research attention.
Retrieve-Rewrite-Answer: A KG-to-Text Enhanced LLMs Framework for Knowledge Graph Question Answering
To this end, we propose an answer-sensitive KG-to-Text approach that can transform KG knowledge into well-textualized statements most informative for KGQA.
Robot Task Planning Based on Large Language Model Representing Knowledge with Directed Graph Structures
Traditional robot task planning methods face challenges when dealing with highly unstructured environments and complex tasks.
Simple or Complex? Complexity-Controllable Question Generation with Soft Templates and Deep Mixture of Experts Model
The ability to generate natural-language questions with controlled complexity levels is highly desirable as it further expands the applicability of question generation.
Adaptive Knowledge-Enhanced Bayesian Meta-Learning for Few-shot Event Detection
Event detection (ED) aims at detecting event trigger words in sentences and classifying them into specific event types.
Ion Clusters and Networks in "Water-in-Salt Electrolytes"
Our model is able to quantitatively reproduce the populations of ionic clusters of different sizes as a function of salt concentration, the critical salt concentration for ionic gelation, and the fraction of ions incorporated into the ionic gel, as observed from molecular simulations of three different lithium-based WiSEs.
Chemical Physics Soft Condensed Matter Statistical Mechanics
Hierarchical Chinese Legal event extraction via Pedal Attention Mechanism
We label a Chinese legal event dataset and evaluate our model on it.
Knowledge-enriched, Type-constrained and Grammar-guided Question Generation over Knowledge Bases
Question generation over knowledge bases (KBQG) aims at generating natural-language questions about a subgraph, i. e. a set of (connected) triples.
Knowledge-aware Method for Confusing Charge Prediction
In this paper, we propose a knowledge-attentive neural network model, which introduces legal schematic knowledge about charges and exploit the knowledge hierarchical representation as the discriminative features to differentiate confusing charges.
