14 citations · 22 across the 8 of their papers we have counts for
8 papers
LoRM: Learning the Language of Rotating Machinery for Self-Supervised Condition Monitoring
Xiao Qin, Xingyi Song, Tong Liu +4
We present LoRM (Language of Rotating Machinery), a self-supervised framework for multi-modal rotating-machinery signal understanding and real-time condition monitoring. LoRM is bu…
ESG-Bench: Benchmarking Long-Context ESG Reports for Hallucination Mitigation
Siqi Sun, Ben Peng Wu, Mali Jin +3
As corporate responsibility increasingly incorporates environmental, social, and governance (ESG) criteria, ESG reporting is becoming a legal requirement in many regions and a key…
A Dataset for Analysing News Framing in Chinese Media
Owen Cook, Yida Mu, Xinye Yang +2
Framing is an essential device in news reporting, allowing the writer to influence public perceptions of current affairs. While there are existing automatic news framing detection…
Efficient Annotator Reliability Assessment and Sample Weighting for Knowledge-Based Misinformation Detection on Social Media
Owen Cook, Charlie Grimshaw, Ben Wu +6
Misinformation spreads rapidly on social media, confusing the truth and targeting potentially vulnerable people. To effectively mitigate the negative impact of misinformation, it m…
Confidence Regulation Neurons in Language Models
Alessandro Stolfo, Ben Wu, Wes Gurnee +4
Despite their widespread use, the mechanisms by which large language models (LLMs) represent and regulate uncertainty in next-token predictions remain largely unexplored. This stud…
Addressing Topic Granularity and Hallucination in Large Language Models for Topic Modelling
Yida Mu, Peizhen Bai, Kalina Bontcheva +1
Large language models (LLMs) with their strong zero-shot topic extraction capabilities offer an alternative to probabilistic topic modelling and closed-set topic classification app…
Large Language Models Offer an Alternative to the Traditional Approach of Topic Modelling
Yida Mu, Chun Dong, Kalina Bontcheva +1
Topic modelling, as a well-established unsupervised technique, has found extensive use in automatically detecting significant topics within a corpus of documents. However, classic…
Don't Waste a Single Annotation: Improving Single-Label Classifiers Through Soft Labels
Ben Wu, Yue Li, Yida Mu +3
In this paper, we address the limitations of the common data annotation and training methods for objective single-label classification tasks. Typically, when annotating such tasks…