61 citations · 135 across the 32 of their papers we have counts for
24 papers · 1 filter
On the Robustness of Document-Level Relation Extraction Models to Entity Name Variations
Shiao Meng, Xuming Hu, Aiwei Liu +4
Driven by the demand for cross-sentence and large-scale relation extraction, document-level relation extraction (DocRE) has attracted increasing research interest. Despite the cont…
LLMArena: Assessing Capabilities of Large Language Models in Dynamic Multi-Agent Environments
Junzhe Chen, Xuming Hu, Shuodi Liu +4
Recent advancements in large language models (LLMs) have revealed their potential for achieving autonomous agents possessing human-level intelligence. However, existing benchmarks…
Evaluating Robustness of Generative Search Engine on Adversarial Factual Questions
Xuming Hu, Xiaochuan Li, Junzhe Chen +8
Generative search engines have the potential to transform how people seek information online, but generated responses from existing large language models (LLMs)-backed generative s…
A Survey of Text Watermarking in the Era of Large Language Models
Aiwei Liu, Leyi Pan, Yijian Lu +7
Text watermarking algorithms are crucial for protecting the copyright of textual content. Historically, their capabilities and application scenarios were limited. However, recent a…
Prompt Me Up: Unleashing the Power of Alignments for Multimodal Entity and Relation Extraction
Xuming Hu, Junzhe Chen, Aiwei Liu +3
How can we better extract entities and relations from text? Using multimodal extraction with images and text obtains more signals for entities and relations, and aligns them throug…
RAPL: A Relation-Aware Prototype Learning Approach for Few-Shot Document-Level Relation Extraction
Shiao Meng, Xuming Hu, Aiwei Liu +4
How to identify semantic relations among entities in a document when only a few labeled documents are available? Few-shot document-level relation extraction (FSDLRE) is crucial for…
Do Large Language Models Know about Facts?
Xuming Hu, Junzhe Chen, Xiaochuan Li +4
Large language models (LLMs) have recently driven striking performance improvements across a range of natural language processing tasks. The factual knowledge acquired during pretr…
An Unforgeable Publicly Verifiable Watermark for Large Language Models
Aiwei Liu, Leyi Pan, Xuming Hu +4
Recently, text watermarking algorithms for large language models (LLMs) have been proposed to mitigate the potential harms of text generated by LLMs, including fake news and copyri…
Exploring the Compositional Generalization in Context Dependent Text-to-SQL Parsing
Aiwei Liu, Wei Liu, Xuming Hu +4
In the context-dependent Text-to-SQL task, the generated SQL statements are refined iteratively based on the user input utterance from each interaction. The input text from each in…
Enhancing Cross-lingual Natural Language Inference by Soft Prompting with Multilingual Verbalizer
Shuang Li, Xuming Hu, Aiwei Liu +4
Cross-lingual natural language inference is a fundamental problem in cross-lingual language understanding. Many recent works have used prompt learning to address the lack of annota…
Gaussian Prior Reinforcement Learning for Nested Named Entity Recognition
Yawen Yang, Xuming Hu, Fukun Ma +4
Named Entity Recognition (NER) is a well and widely studied task in natural language processing. Recently, the nested NER has attracted more attention since its practicality and di…
Read it Twice: Towards Faithfully Interpretable Fact Verification by Revisiting Evidence
Xuming Hu, Zhaochen Hong, Zhijiang Guo +2
Real-world fact verification task aims to verify the factuality of a claim by retrieving evidence from the source document. The quality of the retrieved evidence plays an important…
Give Me More Details: Improving Fact-Checking with Latent Retrieval
Xuming Hu, Junzhe Chen, Zhijiang Guo +1
Evidence plays a crucial role in automated fact-checking. When verifying real-world claims, existing fact-checking systems either assume the evidence sentences are given or use the…
A comprehensive evaluation of ChatGPT's zero-shot Text-to-SQL capability
Aiwei Liu, Xuming Hu, Lijie Wen +1
This paper presents the first comprehensive analysis of ChatGPT's Text-to-SQL ability. Given the recent emergence of large-scale conversational language model ChatGPT and its impre…
Gradient Imitation Reinforcement Learning for General Low-Resource Information Extraction
Xuming Hu, Shiao Meng, Chenwei Zhang +4
Information Extraction (IE) aims to extract structured information from heterogeneous sources. IE from natural language texts include sub-tasks such as Named Entity Recognition (NE…
Character-level White-Box Adversarial Attacks against Transformers via Attachable Subwords Substitution
Aiwei Liu, Honghai Yu, Xuming Hu +5
We propose the first character-level white-box adversarial attack method against transformer models. The intuition of our method comes from the observation that words are split int…
Entity-to-Text based Data Augmentation for various Named Entity Recognition Tasks
Xuming Hu, Yong Jiang, Aiwei Liu +5
Data augmentation techniques have been used to alleviate the problem of scarce labeled data in various NER tasks (flat, nested, and discontinuous NER tasks). Existing augmentation…
Semantic Enhanced Text-to-SQL Parsing via Iteratively Learning Schema Linking Graph
Aiwei Liu, Xuming Hu, Li Lin +1
The generalizability to new databases is of vital importance to Text-to-SQL systems which aim to parse human utterances into SQL statements. Existing works achieve this goal by lev…
CHEF: A Pilot Chinese Dataset for Evidence-Based Fact-Checking
Xuming Hu, Zhijiang Guo, Guanyu Wu +3
The explosion of misinformation spreading in the media ecosystem urges for automated fact-checking. While misinformation spans both geographic and linguistic boundaries, most work…
A Multi-level Supervised Contrastive Learning Framework for Low-Resource Natural Language Inference
Shu'ang Li, Xuming Hu, Li Lin +3
Natural Language Inference (NLI) is a growingly essential task in natural language understanding, which requires inferring the relationship between the sentence pairs (premise and…
HiURE: Hierarchical Exemplar Contrastive Learning for Unsupervised Relation Extraction
Shuliang Liu, Xuming Hu, Chenwei Zhang +3
Unsupervised relation extraction aims to extract the relationship between entities from natural language sentences without prior information on relational scope or distribution. Ex…
Pair-Level Supervised Contrastive Learning for Natural Language Inference
Shu'ang Li, Xuming Hu, Li Lin +1
Natural language inference (NLI) is an increasingly important task for natural language understanding, which requires one to infer the relationship between the sentence pair (premi…
Gradient Imitation Reinforcement Learning for Low Resource Relation Extraction
Xuming Hu, Chenwei Zhang, Yawen Yang +4
Low-resource Relation Extraction (LRE) aims to extract relation facts from limited labeled corpora when human annotation is scarce. Existing works either utilize self-training sche…
A Graph Representation of Semi-structured Data for Web Question Answering
Xingyao Zhang, Linjun Shou, Jian Pei +3
The abundant semi-structured data on the Web, such as HTML-based tables and lists, provide commercial search engines a rich information source for question answering (QA). Differen…