Search Results for author: Xingyi Song

Found 27 papers, 7 papers with code

Confidence Regulation Neurons in Language Models

no code implementations24 Jun 2024 Alessandro Stolfo, Ben Wu, Wes Gurnee, Yonatan Belinkov, Xingyi Song, Mrinmaya Sachan, Neel Nanda

Despite their widespread use, the mechanisms by which large language models (LLMs) represent and regulate uncertainty in next-token predictions remain largely unexplored.

Identifying and Aligning Medical Claims Made on Social Media with Medical Evidence

no code implementations18 May 2024 Anthony Hughes, Xingyi Song

We propose a novel system that can generate synthetic medical claims to aid each of these core tasks.

Addressing Topic Granularity and Hallucination in Large Language Models for Topic Modelling

1 code implementation1 May 2024 Yida Mu, Peizhen Bai, Kalina Bontcheva, Xingyi Song

In this paper, we focus on addressing the issues of topic granularity and hallucinations for better LLM-based topic modelling.

Hallucination Topic Classification

Large Language Models Offer an Alternative to the Traditional Approach of Topic Modelling

no code implementations24 Mar 2024 Yida Mu, Chun Dong, Kalina Bontcheva, Xingyi Song

Topic modelling, as a well-established unsupervised technique, has found extensive use in automatically detecting significant topics within a corpus of documents.

Don't Waste a Single Annotation: Improving Single-Label Classifiers Through Soft Labels

no code implementations9 Nov 2023 Ben Wu, Yue Li, Yida Mu, Carolina Scarton, Kalina Bontcheva, Xingyi Song

In this paper, we address the limitations of the common data annotation and training methods for objective single-label classification tasks.

Examining the Limitations of Computational Rumor Detection Models Trained on Static Datasets

no code implementations20 Sep 2023 Yida Mu, Xingyi Song, Kalina Bontcheva, Nikolaos Aletras

A crucial aspect of a rumor detection model is its ability to generalize, particularly its ability to detect emerging, previously unknown rumors.

Navigating Prompt Complexity for Zero-Shot Classification: A Study of Large Language Models in Computational Social Science

no code implementations23 May 2023 Yida Mu, Ben P. Wu, William Thorne, Ambrose Robinson, Nikolaos Aletras, Carolina Scarton, Kalina Bontcheva, Xingyi Song

Instruction-tuned Large Language Models (LLMs) have exhibited impressive language understanding and the capacity to generate responses that follow specific prompts.

Zero-Shot Learning

Examining Temporalities on Stance Detection towards COVID-19 Vaccination

no code implementations10 Apr 2023 Yida Mu, Mali Jin, Kalina Bontcheva, Xingyi Song

It is crucial for policymakers to have a comprehensive understanding of the public's stance towards vaccination on a large scale.

Stance Classification Stance Detection

A Large-Scale Comparative Study of Accurate COVID-19 Information versus Misinformation

no code implementations10 Apr 2023 Yida Mu, Ye Jiang, Freddy Heppell, Iknoor Singh, Carolina Scarton, Kalina Bontcheva, Xingyi Song

This motivated us to carry out a comparative study of the characteristics of COVID-19 misinformation versus those of accurate COVID-19 information through a large-scale computational analysis of over 242 million tweets.

Misinformation

SheffieldVeraAI at SemEval-2023 Task 3: Mono and multilingual approaches for news genre, topic and persuasion technique classification

1 code implementation16 Mar 2023 Ben Wu, Olesya Razuvayevskaya, Freddy Heppell, João A. Leite, Carolina Scarton, Kalina Bontcheva, Xingyi Song

For Subtask 2 (Framing), we achieved first place in 3 languages, and the best average rank across all the languages, by using two separate ensembles: a monolingual RoBERTa-MUPPETLARGE and an ensemble of XLM-RoBERTaLARGE with adapters and task adaptive pretraining.

VaxxHesitancy: A Dataset for Studying Hesitancy towards COVID-19 Vaccination on Twitter

1 code implementation17 Jan 2023 Yida Mu, Mali Jin, Charlie Grimshaw, Carolina Scarton, Kalina Bontcheva, Xingyi Song

Annotated data is also necessary for training data-driven models for more nuanced analysis of attitudes towards vaccination.

Language Modelling

Classifying COVID-19 vaccine narratives

no code implementations18 Jul 2022 Yue Li, Carolina Scarton, Xingyi Song, Kalina Bontcheva

This paper addresses the need for monitoring and analysing vaccine narratives online by introducing a novel vaccine narrative classification task, which categorises COVID-19 vaccine claims into one of seven categories.

Data Augmentation

An Exploratory Study on Utilising the Web of Linked Data for Product Data Mining

no code implementations3 Sep 2021 Ziqi Zhang, Xingyi Song

We process billions of structured data points in the form of RDF n-quads, to create multi-million words of product-related corpora that are later used in three different ways for creating of language resources: training word embedding models, continued pre-training of BERT-like language models, and training Machine Translation models that are used as a proxy to generate product-related keywords.

Machine Translation Word Embeddings

Categorising Fine-to-Coarse Grained Misinformation: An Empirical Study of COVID-19 Infodemic

no code implementations22 Jun 2021 Ye Jiang, Xingyi Song, Carolina Scarton, Ahmet Aker, Kalina Bontcheva

In this paper, we introduce a fine-grained annotated misinformation tweets dataset including social behaviours annotation (e. g. comment or question to the misinformation).

Misinformation

Classification Aware Neural Topic Model and its Application on a New COVID-19 Disinformation Corpus

no code implementations5 Jun 2020 Xingyi Song, Johann Petrak, Ye Jiang, Iknoor Singh, Diana Maynard, Kalina Bontcheva

The explosion of disinformation accompanying the COVID-19 pandemic has overloaded fact-checkers and media worldwide, and brought a new major challenge to government responses worldwide.

Fact Checking General Classification

Using Deep Neural Networks with Intra- and Inter-Sentence Context to Classify Suicidal Behaviour

no code implementations LREC 2020 Xingyi Song, Johnny Downs, Sumithra Velupillai, Rachel Holden, Maxim Kikoler, Kalina Bontcheva, Rina Dutta, Angus Roberts

Identifying statements related to suicidal behaviour in psychiatric electronic health records (EHRs) is an important step when modeling that behaviour, and when assessing suicide risk.

Classification General Classification +1

RP-DNN: A Tweet level propagation context based deep neural networks for early rumor detection in Social Media

1 code implementation LREC 2020 Jie Gao, Sooji Han, Xingyi Song, Fabio Ciravegna

Early rumor detection (ERD) on social media platform is very challenging when limited, incomplete and noisy information is available.

Language Modelling

Bio-YODIE: A Named Entity Linking System for Biomedical Text

1 code implementation12 Nov 2018 Genevieve Gorrell, Xingyi Song, Angus Roberts

Ever-expanding volumes of biomedical text require automated semantic annotation techniques to curate and put to best use.

Entity Linking

A Deep Neural Network Sentence Level Classification Method with Context Information

no code implementations EMNLP 2018 Xingyi Song, Johann Petrak, Angus Roberts

In the sentence classification task, context formed from sentences adjacent to the sentence being classified can provide important information for classification.

Classification General Classification +2

Cannot find the paper you are looking for? You can Submit a new open access paper.