Search Results for author:
Found 50 papers, 23 papers with code
Event Causality Identification via Derivative Prompt Joint Learning
This paper studies event causality identification, which aims at predicting the causality relation for a pair of events in a sentence.
PRIMO: Progressive Induction for Multi-hop Open Rule Generation
Open rule refer to the implication from premise atoms to hypothesis atoms, which captures various relations between instances in the real world.
UniHGKR: Unified Instruction-aware Heterogeneous Knowledge Retrievers
In this paper, we introduce UniHGKR, a unified instruction-aware heterogeneous knowledge retriever that (1) builds a unified retrieval space for heterogeneous knowledge and (2) follows diverse user instructions to retrieve knowledge of specified types.
CoTKR: Chain-of-Thought Enhanced Knowledge Rewriting for Complex Knowledge Graph Question Answering
Recent studies have explored the use of Large Language Models (LLMs) with Retrieval Augmented Generation (RAG) for Knowledge Graph Question Answering (KGQA).
A Comprehensive Survey on Root Cause Analysis in (Micro) Services: Methodologies, Challenges, and Trends
The complex dependencies and propagative faults inherent in microservices, characterized by a dense network of interconnected services, pose significant challenges in identifying the underlying causes of issues.
Can Large Language Models Understand DL-Lite Ontologies? An Empirical Study
With extensive experiments, we demonstrate both the effectiveness and limitations of LLMs in understanding DL-Lite ontologies.
Single Image Unlearning: Efficient Machine Unlearning in Multimodal Large Language Models
Alongside our method, we establish MMUBench, a new benchmark for MU in MLLMs and introduce a collection of metrics for its evaluation.
Double Mixture: Towards Continual Event Detection from Speech
To address the challenges of catastrophic forgetting and effective disentanglement, we propose a novel method, 'Double Mixture.'
HeGTa: 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.
MATEval: A Multi-Agent Discussion Framework for Advancing Open-Ended Text Evaluation
Experimental results show that our framework outperforms existing open-ended text evaluation methods and achieves the highest correlation with human evaluation, which confirms the effectiveness and advancement of our framework in addressing the uncertainties and instabilities in evaluating LLMs-generated text.
DEE: Dual-stage Explainable Evaluation Method for Text Generation
Automatic methods for evaluating machine-generated texts hold significant importance due to the expanding applications of generative systems.
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).
Large Language Models Can Better Understand Knowledge Graphs Than We Thought
To elucidate this, we design a series of experiments to explore LLMs' understanding of different KG input formats within the context of prompt engineering.
Question Answering Over Spatio-Temporal Knowledge Graph
While the research community's focus on Knowledge Graph Question Answering (KGQA), the field of answering questions incorporating both spatio-temporal information based on STKGs remains largely unexplored.
KGroot: Enhancing Root Cause Analysis through Knowledge Graphs and Graph Convolutional Neural Networks
To achieve this, KGroot uses event knowledge and the correlation between events to perform root cause reasoning by integrating knowledge graphs and GCNs for RCA.
The Role of LLMs in Sustainable Smart Cities: Applications, Challenges, and Future Directions
Smart cities stand as pivotal components in the ongoing pursuit of elevating urban living standards, facilitating the rapid expansion of urban areas while efficiently managing resources through sustainable and scalable innovations.
Towards Event Extraction from Speech with Contextual Clues
While text-based event extraction has been an active research area and has seen successful application in many domains, extracting semantic events from speech directly is an under-explored problem.
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.
Embedding Ontologies via Incorporating Extensional and Intensional Knowledge
Extensional knowledge provides information about the concrete instances that belong to specific concepts in the ontology, while intensional knowledge details inherent properties, characteristics, and semantic associations among concepts.
Ontology Revision based on Pre-trained Language Models
We conduct experiments over 19 ontology pairs and compare our algorithms and scoring functions with existing ones.
Incorporating Domain Knowledge Graph into Multimodal Movie Genre Classification with Self-Supervised Attention and Contrastive Learning
Firstly we retrieve the relevant embedding from the knowledge graph by utilizing group relations in metadata and then integrate it with other modalities.
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.
DoG-Instruct: Towards Premium Instruction-Tuning Data via Text-Grounded Instruction Wrapping
In particular, compared to the best-performing baseline, the LLM trained using our generated dataset exhibits a 10\% relative improvement in performance on AlpacaEval, despite utilizing only 1/5 of its training data.
An Embedding-based Approach to Inconsistency-tolerant Reasoning with Inconsistent Ontologies
A natural way to reason with an inconsistent ontology is to utilize the maximal consistent subsets of the ontology.
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).
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
Question Answering
on WebQuestionsSP
Learn from Yesterday: A Semi-Supervised Continual Learning Method for Supervision-Limited Text-to-SQL Task Streams
The first solution Vanilla is to perform self-training, augmenting the supervised training data with predicted pseudo-labeled instances of the current task, while replacing the full volume retraining with episodic memory replay to balance the training efficiency with the performance of previous tasks.
Towards Relation Extraction From Speech
We explore speech relation extraction via two approaches: the pipeline approach conducting text-based extraction with a pretrained ASR module, and the end2end approach via a new proposed encoder-decoder model, or what we called SpeechRE.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Neural Topic Modeling with Deep Mutual Information Estimation
NTM-DMIE is a neural network method for topic learning which maximizes the mutual information between the input documents and their latent topic representation.
Conditional Generation Net for Medication Recommendation
Medication recommendation targets to provide a proper set of medicines according to patients' diagnoses, which is a critical task in clinics.
Active Learning for Event Extraction with Memory-based Loss Prediction Model
During the selection process, we use an internal-external sample loss ranking method to evaluate the sample importance by using local information.
Outlining and Filling: Hierarchical Query Graph Generation for Answering Complex Questions over Knowledge Graphs
The high-level decoding generates an AQG as a constraint to prune the search space and reduce the locally ambiguous query graph.
Ranked #1 on
Knowledge Base Question Answering
on LC-QuAD 1.0
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.
Pretrained Language Model in Continual Learning: A Comparative Study
In this paper, we thoroughly compare the continual learning performance over the combination of 5 PLMs and 4 veins of CL methods on 3 benchmarks in 2 typical incremental settings.
Leveraging Table Content for Zero-shot Text-to-SQL with Meta-Learning
Compared to the larger pre-trained model and the tabular-specific pre-trained model, our approach is still competitive.
Formal Query Building with Query Structure Prediction for Complex Question Answering over Knowledge Base
However, this candidate generation strategy ignores the structure of queries, resulting in a considerable number of noisy queries.
Ranked #2 on
Knowledge Base Question Answering
on LC-QuAD 1.0
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.
Natural Language Understanding for Argumentative Dialogue Systems in the Opinion Building Domain
Intent classifier model stacks BiLSTM with attention mechanism on top of the pre-trained BERT model and fine-tune the model for recognizing the user intent, whereas the argument similarity model employs BERT+BiLSTM for identifying system arguments the user refers to in his or her natural language utterances.
Curriculum-Meta Learning for Order-Robust Continual Relation Extraction
We propose a novel curriculum-meta learning method to tackle the above two challenges in continual relation extraction.
Hierarchical Chinese Legal event extraction via Pedal Attention Mechanism
We label a Chinese legal event dataset and evaluate our model on it.
Less is More: Data-Efficient Complex Question Answering over Knowledge Bases
Our framework consists of a neural generator and a symbolic executor that, respectively, transforms a natural-language question into a sequence of primitive actions, and executes them over the knowledge base to compute the answer.
Retrieve, Program, Repeat: Complex Knowledge Base Question Answering via Alternate Meta-learning
However, this comes at the cost of manually labeling similar questions to learn a retrieval model, which is tedious and expensive.
Few-Shot Complex Knowledge Base Question Answering via Meta Reinforcement Learning
Our method achieves state-of-the-art performance on the CQA dataset (Saha et al., 2018) while using only five trial trajectories for the top-5 retrieved questions in each support set, and metatraining on tasks constructed from only 1% of the training set.
Knowledge Base Question Answering
Meta Reinforcement Learning
+3
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.
Revealing Secrets in SPARQL Session Level
Based on Semantic Web technologies, knowledge graphs help users to discover information of interest by using live SPARQL services.
Overview of the CCKS 2019 Knowledge Graph Evaluation Track: Entity, Relation, Event and QA
Knowledge graph models world knowledge as concepts, entities, and the relationships between them, which has been widely used in many real-world tasks.
Efficiently Embedding Dynamic Knowledge Graphs
Knowledge graph (KG) embedding encodes the entities and relations from a KG into low-dimensional vector spaces to support various applications such as KG completion, question answering, and recommender systems.
A Distance-based Paraconsistent Semantics for DL-Lite
DL-Lite is an important family of description logics.
