Search Results for author:
Found 41 papers, 21 papers with code
WaterVG: Waterway Visual Grounding based on Text-Guided Vision and mmWave Radar
The pattern of text-guided two sensors equips a finer granularity of text prompts with visual and radar features of referred targets.
UltraWiki: Ultra-fine-grained Entity Set Expansion with Negative Seed Entities
To solve this issue, we first introduce negative seed entities in the inputs, which belong to the same fine-grained semantic class as the positive seed entities but differ in certain attributes.
Unraveling Babel: Exploring Multilingual Activation Patterns within Large Language Models
Recently, large language models (LLMs) have achieved tremendous breakthroughs in the field of language processing, yet their mechanisms in processing multiple languages remain agnostic.
LLMArena: Assessing Capabilities of Large Language Models in Dynamic Multi-Agent Environments
Recent advancements in large language models (LLMs) have revealed their potential for achieving autonomous agents possessing human-level intelligence.
Evaluating Robustness of Generative Search Engine on Adversarial Factual Questions
To this end, we propose evaluating the robustness of generative search engines in the realistic and high-risk setting, where adversaries have only black-box system access and seek to deceive the model into returning incorrect responses.
Rethinking the Roles of Large Language Models in Chinese Grammatical Error Correction
To promote the CGEC field to better adapt to the era of LLMs, we rethink the roles of LLMs in the CGEC task so that they can be better utilized and explored in CGEC.
When LLMs Meet Cunning Questions: A Fallacy Understanding Benchmark for Large Language Models
In this paper, we challenge the reasoning and understanding abilities of LLMs by proposing a FaLlacy Understanding Benchmark (FLUB) containing cunning questions that are easy for humans to understand but difficult for models to grasp.
A Survey of Text Watermarking in the Era of Large Language Models
Text watermarking algorithms play a crucial role in the copyright protection of textual content, yet their capabilities and application scenarios have been limited historically.
Mind's Mirror: Distilling Self-Evaluation Capability and Comprehensive Thinking from Large Language Models
While techniques such as chain-of-thought (CoT) distillation have displayed promise in distilling LLMs into small language models (SLMs), there is a risk that distilled SLMs may still inherit flawed reasoning and hallucinations from LLMs.
Prompt Me Up: Unleashing the Power of Alignments for Multimodal Entity and Relation Extraction
Additionally, our method is orthogonal to previous multimodal fusions, and using it on prior SOTA fusions further improves 5. 47% F1.
RAPL: A Relation-Aware Prototype Learning Approach for Few-Shot Document-Level Relation Extraction
However, existing works often struggle to obtain class prototypes with accurate relational semantics: 1) To build prototype for a target relation type, they aggregate the representations of all entity pairs holding that relation, while these entity pairs may also hold other relations, thus disturbing the prototype.
Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity
This survey addresses the crucial issue of factuality in Large Language Models (LLMs).
A Semantic Invariant Robust Watermark for Large Language Models
In this work, we propose a semantic invariant watermarking method for LLMs that provides both attack robustness and security robustness.
Do Large Language Models Know about Facts?
Large language models (LLMs) have recently driven striking performance improvements across a range of natural language processing tasks.
An Unforgeable Publicly Verifiable Watermark for Large Language Models
Experiments demonstrate that our algorithm attains high detection accuracy and computational efficiency through neural networks with a minimized number of parameters.
Exploring the Compositional Generalization in Context Dependent Text-to-SQL Parsing
Based on these observations, we propose a method named \texttt{p-align} to improve the compositional generalization of Text-to-SQL models.
GDA: Generative Data Augmentation Techniques for Relation Extraction Tasks
These techniques neither preserve the semantic consistency of the original sentences when rule-based augmentations are adopted, nor preserve the syntax structure of sentences when expressing relations using seq2seq models, resulting in less diverse augmentations.
Multimodal Relation Extraction with Cross-Modal Retrieval and Synthesis
Multimodal relation extraction (MRE) is the task of identifying the semantic relationships between two entities based on the context of the sentence image pair.
Give Me More Details: Improving Fact-Checking with Latent Retrieval
Evidence plays a crucial role in automated fact-checking.
Enhancing Cross-lingual Natural Language Inference by Soft Prompting with Multilingual Verbalizer
In this paper, we propose a novel Soft prompt learning framework with the Multilingual Verbalizer (SoftMV) for XNLI.
Cross-Lingual Natural Language Inference
Cross-Lingual Transfer
Gaussian Prior Reinforcement Learning for Nested Named Entity Recognition
Existing works for nested NER ignore the recognition order and boundary position relation of nested entities.
Read it Twice: Towards Faithfully Interpretable Fact Verification by Revisiting Evidence
In light of this, we propose a fact verification model named ReRead to retrieve evidence and verify claim that: (1) Train the evidence retriever to obtain interpretable evidence (i. e., faithfulness and plausibility criteria); (2) Train the claim verifier to revisit the evidence retrieved by the optimized evidence retriever to improve the accuracy.
Think Rationally about What You See: Continuous Rationale Extraction for Relation Extraction
Relation extraction (RE) aims to extract potential relations according to the context of two entities, thus, deriving rational contexts from sentences plays an important role.
A comprehensive evaluation of ChatGPT's zero-shot Text-to-SQL capability
This paper presents the first comprehensive analysis of ChatGPT's Text-to-SQL ability.
Gradient Imitation Reinforcement Learning for General Low-Resource Information Extraction
Low-Resource Information Extraction (LRIE) strives to use unsupervised data, reducing the required resources and human annotation.
Query-based Instance Discrimination Network for Relational Triple Extraction
Joint entity and relation extraction has been a core task in the field of information extraction.
Contrastive Learning
Joint Entity and Relation Extraction
+1
Character-level White-Box Adversarial Attacks against Transformers via Attachable Subwords Substitution
We propose the first character-level white-box adversarial attack method against transformer models.
Entity-to-Text based Data Augmentation for various Named Entity Recognition Tasks
Data augmentation techniques have been used to alleviate the problem of scarce labeled data in various NER tasks (flat, nested, and discontinuous NER tasks).
Scene Graph Modification as Incremental Structure Expanding
A scene graph is a semantic representation that expresses the objects, attributes, and relationships between objects in a scene.
Domain-Specific NER via Retrieving Correlated Samples
Successful Machine Learning based Named Entity Recognition models could fail on texts from some special domains, for instance, Chinese addresses and e-commerce titles, where requires adequate background knowledge.
Semantic Enhanced Text-to-SQL Parsing via Iteratively Learning Schema Linking Graph
First, we extract a schema linking graph from PLMs through a probing procedure in an unsupervised manner.
Graph Component Contrastive Learning for Concept Relatedness Estimation
As it is intractable for data augmentation to fully capture the structural information of the ConcreteGraph due to a large amount of potential concept pairs, we further introduce a novel Graph Component Contrastive Learning framework to implicitly learn the complete structure of the ConcreteGraph.
CHEF: A Pilot Chinese Dataset for Evidence-Based Fact-Checking
The explosion of misinformation spreading in the media ecosystem urges for automated fact-checking.
A Multi-level Supervised Contrastive Learning Framework for Low-Resource Natural Language Inference
Natural Language Inference (NLI) is a growingly essential task in natural language understanding, which requires inferring the relationship between the sentence pairs (premise and hypothesis).
HiURE: Hierarchical Exemplar Contrastive Learning for Unsupervised Relation Extraction
Unsupervised relation extraction aims to extract the relationship between entities from natural language sentences without prior information on relational scope or distribution.
Graph Neural Network with Curriculum Learning for Imbalanced Node Classification
Graph Neural Network (GNN) is an emerging technique for graph-based learning tasks such as node classification.
Pair-Level Supervised Contrastive Learning for Natural Language Inference
We adopt a cross attention module to learn the joint representations of the sentence pairs.
What Makes the Story Forward? Inferring Commonsense Explanations as Prompts for Future Event Generation
To alleviate the knowledge forgetting issue, we design two modules, Im and Gm, for each type of knowledge, which are combined via prompt tuning.
Gradient Imitation Reinforcement Learning for Low Resource Relation Extraction
Low-resource Relation Extraction (LRE) aims to extract relation facts from limited labeled corpora when human annotation is scarce.
Semi-supervised Relation Extraction via Incremental Meta Self-Training
To alleviate human efforts from obtaining large-scale annotations, Semi-Supervised Relation Extraction methods aim to leverage unlabeled data in addition to learning from limited samples.
SelfORE: Self-supervised Relational Feature Learning for Open Relation Extraction
Open relation extraction is the task of extracting open-domain relation facts from natural language sentences.
