5 papers
Teaching Language Models to Check Grounded Claim Factuality with Human Test-Taking Strategies
Yuxuan Ye, Raul Santos-Rodriguez, Edwin Simpson
Grounded claim factuality checking is important for large language model (LLM) applications such as retrieval-augmented generation, as it helps users assess the correctness of gene…
Optimising Factual Consistency in Summarisation via Preference Learning from Multiple Imperfect Metrics
Yuxuan Ye, Raul Santos-Rodriguez, Edwin Simpson
Reinforcement learning with evaluation metrics as rewards is widely used to enhance specific capabilities of language models. However, for tasks such as factually consistent summar…
Machine Learning for Climate Policy: Understanding Policy Progression in the European Green Deal
Patricia West, Michelle WL Wan, Alexander Hepburn +3
Climate change demands effective legislative action to mitigate its impacts. This study explores the application of machine learning (ML) to understand the progression of climate p…
Using Similarity to Evaluate Factual Consistency in Summaries
Yuxuan Ye, Edwin Simpson, Raul Santos Rodriguez
Cutting-edge abstractive summarisers generate fluent summaries, but the factuality of the generated text is not guaranteed. Early summary factuality evaluation metrics are usually…
Towards Abstractive Timeline Summarisation using Preference-based Reinforcement Learning
Yuxuan Ye, Edwin Simpson
This paper introduces a novel pipeline for summarising timelines of events reported by multiple news sources. Transformer-based models for abstractive summarisation generate cohere…