Search Results for author: Xuming Hu

Found 43 papers, 24 papers with code

MarkLLM: An Open-Source Toolkit for LLM Watermarking

1 code implementation16 May 2024 Leyi Pan, Aiwei Liu, Zhiwei He, Zitian Gao, Xuandong Zhao, Yijian Lu, Binglin Zhou, Shuliang Liu, Xuming Hu, Lijie Wen, Irwin King

However, the abundance of LLM watermarking algorithms, their intricate mechanisms, and the complex evaluation procedures and perspectives pose challenges for researchers and the community to easily experiment with, understand, and assess the latest advancements.

UltraWiki: Ultra-fine-grained Entity Set Expansion with Negative Seed Entities

1 code implementation7 Mar 2024 Yangning Li, Qingsong Lv, Tianyu Yu, Yinghui Li, Shulin Huang, Tingwei Lu, Xuming Hu, Wenhao Jiang, Hai-Tao Zheng, Hui Wang

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.

Attribute Contrastive Learning +1

Unraveling Babel: Exploring Multilingual Activation Patterns within Large Language Models

no code implementations26 Feb 2024 Weize Liu, Yinlong Xu, Hongxia Xu, Jintai Chen, Xuming Hu, Jian Wu

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

1 code implementation26 Feb 2024 Junzhe Chen, Xuming Hu, Shuodi Liu, Shiyu Huang, Wei-Wei Tu, Zhaofeng He, Lijie Wen

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

no code implementations25 Feb 2024 Xuming Hu, Xiaochuan Li, Junzhe Chen, Yinghui Li, Yangning Li, Xiaoguang Li, Yasheng Wang, Qun Liu, Lijie Wen, Philip S. Yu, Zhijiang Guo

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.

Retrieval

Rethinking the Roles of Large Language Models in Chinese Grammatical Error Correction

no code implementations18 Feb 2024 Yinghui Li, Shang Qin, Jingheng Ye, Shirong Ma, Yangning Li, Libo Qin, Xuming Hu, Wenhao Jiang, Hai-Tao Zheng, Philip S. Yu

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.

Grammatical Error Correction

When LLMs Meet Cunning Texts: A Fallacy Understanding Benchmark for Large Language Models

1 code implementation16 Feb 2024 Yinghui Li, Qingyu Zhou, Yuanzhen Luo, Shirong Ma, Yangning Li, Hai-Tao Zheng, Xuming Hu, Philip S. Yu

In this paper, we challenge the reasoning and understanding abilities of LLMs by proposing a FaLlacy Understanding Benchmark (FLUB) containing cunning texts 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

no code implementations13 Dec 2023 Aiwei Liu, Leyi Pan, Yijian Lu, Jingjing Li, Xuming Hu, Xi Zhang, Lijie Wen, Irwin King, Hui Xiong, Philip S. Yu

Text watermarking algorithms play a crucial role in the copyright protection of textual content, yet their capabilities and application scenarios have been limited historically.

Dialogue Generation

Mind's Mirror: Distilling Self-Evaluation Capability and Comprehensive Thinking from Large Language Models

1 code implementation15 Nov 2023 Weize Liu, Guocong Li, Kai Zhang, Bang Du, Qiyuan Chen, Xuming Hu, Hongxia Xu, Jintai Chen, Jian Wu

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.

Transfer Learning

Prompt Me Up: Unleashing the Power of Alignments for Multimodal Entity and Relation Extraction

1 code implementation25 Oct 2023 Xuming Hu, Junzhe Chen, Aiwei Liu, Shiao Meng, Lijie Wen, Philip S. Yu

Additionally, our method is orthogonal to previous multimodal fusions, and using it on prior SOTA fusions further improves 5. 47% F1.

Relation Relation Extraction

RAPL: A Relation-Aware Prototype Learning Approach for Few-Shot Document-Level Relation Extraction

1 code implementation24 Oct 2023 Shiao Meng, Xuming Hu, Aiwei Liu, Shu'ang Li, Fukun Ma, Yawen Yang, Lijie Wen

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.

Document-level Relation Extraction Meta-Learning +1

A Semantic Invariant Robust Watermark for Large Language Models

2 code implementations10 Oct 2023 Aiwei Liu, Leyi Pan, Xuming Hu, Shiao Meng, Lijie Wen

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?

no code implementations8 Oct 2023 Xuming Hu, Junzhe Chen, Xiaochuan Li, Yufei Guo, Lijie Wen, Philip S. Yu, Zhijiang Guo

Large language models (LLMs) have recently driven striking performance improvements across a range of natural language processing tasks.

Question Answering Text Generation

An Unforgeable Publicly Verifiable Watermark for Large Language Models

3 code implementations30 Jul 2023 Aiwei Liu, Leyi Pan, Xuming Hu, Shu'ang Li, Lijie Wen, Irwin King, Philip S. Yu

Experiments demonstrate that our algorithm attains high detection accuracy and computational efficiency through neural networks.

Computational Efficiency

Exploring the Compositional Generalization in Context Dependent Text-to-SQL Parsing

no code implementations29 May 2023 Aiwei Liu, Wei Liu, Xuming Hu, Shuang Li, Fukun Ma, Yawen Yang, Lijie Wen

Based on these observations, we propose a method named \texttt{p-align} to improve the compositional generalization of Text-to-SQL models.

SQL Parsing Text-To-SQL

GDA: Generative Data Augmentation Techniques for Relation Extraction Tasks

no code implementations26 May 2023 Xuming Hu, Aiwei Liu, Zeqi Tan, Xin Zhang, Chenwei Zhang, Irwin King, Philip S. Yu

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.

Data Augmentation Relation +1

Multimodal Relation Extraction with Cross-Modal Retrieval and Synthesis

no code implementations25 May 2023 Xuming Hu, Zhijiang Guo, Zhiyang Teng, Irwin King, Philip S. Yu

Multimodal relation extraction (MRE) is the task of identifying the semantic relationships between two entities based on the context of the sentence image pair.

Cross-Modal Retrieval Object +4

Think Rationally about What You See: Continuous Rationale Extraction for Relation Extraction

1 code implementation2 May 2023 Xuming Hu, Zhaochen Hong, Chenwei Zhang, Irwin King, Philip S. Yu

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.

counterfactual Relation +2

Read it Twice: Towards Faithfully Interpretable Fact Verification by Revisiting Evidence

1 code implementation2 May 2023 Xuming Hu, Zhaochen Hong, Zhijiang Guo, Lijie Wen, Philip S. Yu

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.

Claim Verification Decision Making +1

Entity-to-Text based Data Augmentation for various Named Entity Recognition Tasks

no code implementations19 Oct 2022 Xuming Hu, Yong Jiang, Aiwei Liu, Zhongqiang Huang, Pengjun Xie, Fei Huang, Lijie Wen, Philip S. Yu

Data augmentation techniques have been used to alleviate the problem of scarce labeled data in various NER tasks (flat, nested, and discontinuous NER tasks).

Data Augmentation named-entity-recognition +3

Scene Graph Modification as Incremental Structure Expanding

no code implementations COLING 2022 Xuming Hu, Zhijiang Guo, Yu Fu, Lijie Wen, Philip S. Yu

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

1 code implementation COLING 2022 Xin Zhang, Yong Jiang, Xiaobin Wang, Xuming Hu, Yueheng Sun, Pengjun Xie, Meishan Zhang

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.

Named Entity Recognition

Semantic Enhanced Text-to-SQL Parsing via Iteratively Learning Schema Linking Graph

1 code implementation8 Aug 2022 Aiwei Liu, Xuming Hu, Li Lin, Lijie Wen

First, we extract a schema linking graph from PLMs through a probing procedure in an unsupervised manner.

Graph Learning SQL Parsing +1

Graph Component Contrastive Learning for Concept Relatedness Estimation

1 code implementation25 Jun 2022 Yueen Ma, Zixing Song, Xuming Hu, Jingjing Li, Yifei Zhang, Irwin King

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.

Contrastive Learning Data Augmentation +2

A Multi-level Supervised Contrastive Learning Framework for Low-Resource Natural Language Inference

no code implementations31 May 2022 Shu'ang Li, Xuming Hu, Li Lin, Aiwei Liu, Lijie Wen, Philip S. Yu

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).

Contrastive Learning Data Augmentation +5

HiURE: Hierarchical Exemplar Contrastive Learning for Unsupervised Relation Extraction

1 code implementation NAACL 2022 Xuming Hu, Shuliang Liu, Chenwei Zhang, Shu`ang Li, Lijie Wen, Philip S. Yu

Unsupervised relation extraction aims to extract the relationship between entities from natural language sentences without prior information on relational scope or distribution.

Clustering Contrastive Learning +3

What Makes the Story Forward? Inferring Commonsense Explanations as Prompts for Future Event Generation

no code implementations18 Jan 2022 Li Lin, Yixin Cao, Lifu Huang, Shu'ang Li, Xuming Hu, Lijie Wen, Jianmin Wang

To alleviate the knowledge forgetting issue, we design two modules, Im and Gm, for each type of knowledge, which are combined via prompt tuning.

Information Retrieval Retrieval +1

Gradient Imitation Reinforcement Learning for Low Resource Relation Extraction

1 code implementation EMNLP 2021 Xuming Hu, Chenwei Zhang, Yawen Yang, Xiaohe Li, Li Lin, Lijie Wen, Philip S. Yu

Low-resource Relation Extraction (LRE) aims to extract relation facts from limited labeled corpora when human annotation is scarce.

Meta-Learning Pseudo Label +5

Semi-supervised Relation Extraction via Incremental Meta Self-Training

1 code implementation Findings (EMNLP) 2021 Xuming Hu, Chenwei Zhang, Fukun Ma, Chenyao Liu, Lijie Wen, Philip S. Yu

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.

Meta-Learning Pseudo Label +2

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