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Found 17 papers, 7 papers with code
DKPLM: Decomposable Knowledge-enhanced Pre-trained Language Model for Natural Language Understanding
Knowledge-Enhanced Pre-trained Language Models (KEPLMs) are pre-trained models with relation triples injecting from knowledge graphs to improve language understanding abilities.
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.
Can ChatGPT Replace Traditional KBQA Models? An In-depth Analysis of the Question Answering Performance of the GPT LLM Family
ChatGPT is a powerful large language model (LLM) that covers knowledge resources such as Wikipedia and supports natural language question answering using its own knowledge.
Ranked #1 on
Knowledge Base Question Answering
on WebQuestionsSP
(Accuracy metric)
HiCLRE: A Hierarchical Contrastive Learning Framework for Distantly Supervised Relation Extraction
In this paper, we propose a hierarchical contrastive learning Framework for Distantly Supervised relation extraction (HiCLRE) to reduce noisy sentences, which integrate the global structural information and local fine-grained interaction.
An Empirical Study of Pre-trained Language Models in Simple Knowledge Graph Question Answering
Large-scale pre-trained language models (PLMs) such as BERT have recently achieved great success and become a milestone in natural language processing (NLP).
Segmentation and Tracking of Vegetable Plants by Exploiting Vegetable Shape Feature for Precision Spray of Agricultural Robots
Regarding the robust tracking of vegetable plants, to solve the challenging problem of associating vegetables with similar color and texture in consecutive images, in this paper, a novel method of Multiple Object Tracking and Segmentation (MOTS) is proposed for instance segmentation and tracking of multiple vegetable plants.
Trainable Joint Channel Estimation, Detection and Decoding for MIMO URLLC Systems
The receiver design for multi-input multi-output (MIMO) ultra-reliable and low-latency communication (URLLC) systems can be a tough task due to the use of short channel codes and few pilot symbols.
Graph Matching with Anchor Nodes: A Learning Approach
In this paper, we consider the weighted graph matching problem with partially disclosed correspondences between a number of anchor nodes.
Stable and Informative Spectral Signatures for Graph Matching
We also introduce the pairwise heat kernel distance as a stable second order compatibility term; we justify its plausibility by showing that in a certain limiting case it converges to the classical adjacency matrix-based second order compatibility function.
Distributable Consistent Multi-Object Matching
In this paper we propose an optimization-based framework to multiple object matching.
Benchmarking off-the-shelf statistical shape modeling tools in clinical applications
Statistical shape modeling (SSM) is widely used in biology and medicine as a new generation of morphometric approaches for the quantitative analysis of anatomical shapes.
Dual-Channel Evidence Fusion for Fact Verification over Texts and Tables
Different from previous fact extraction and verification tasks that only consider evidence of a single format, FEVEROUS brings further challenges by extending the evidence format to both plain text and tables.
Robust MIMO Detection With Imperfect CSI: A Neural Network Solution
Furthermore, a robust detection network RADMMNet is constructed by unfolding the ADMM iterations and employing both model-driven and data-driven philosophies.
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.
Exploring the Impact of Table-to-Text Methods on Augmenting LLM-based Question Answering with Domain Hybrid Data
Table-to-Text Generation is a promising solution by facilitating the transformation of hybrid data into a uniformly text-formatted corpus.
MIKE: A New Benchmark for Fine-grained Multimodal Entity Knowledge Editing
Multimodal knowledge editing represents a critical advancement in enhancing the capabilities of Multimodal Large Language Models (MLLMs).
HGT: Leveraging Heterogeneous Graph-enhanced Large Language Models for Few-shot Complex Table Understanding
Table understanding (TU) has achieved promising advancements, but it faces the challenges of the scarcity of manually labeled tables and the presence of complex table structures. To address these challenges, we propose HGT, a framework with a heterogeneous graph (HG)-enhanced large language model (LLM) to tackle few-shot TU tasks. It leverages the LLM by aligning the table semantics with the LLM's parametric knowledge through soft prompts and instruction turning and deals with complex tables by a multi-task pre-training scheme involving three novel multi-granularity self-supervised HG pre-training objectives. We empirically demonstrate the effectiveness of HGT, showing that it outperforms the SOTA for few-shot complex TU on several benchmarks.
