14 citations · 36 across the 9 of their papers we have counts for
9 papers
CSS: Contrastive Semantic Similarity for Uncertainty Quantification of LLMs
Shuang Ao, Stefan Rueger, Advaith Siddharthan
Despite the impressive capability of large language models (LLMs), knowing when to trust their generations remains an open challenge. The recent literature on uncertainty quantific…
Textual Summarisation of Large Sets: Towards a General Approach
Kittipitch Kuptavanich, Ehud Reiter, Kees Van Deemter +1
We are developing techniques to generate summary descriptions of sets of objects. In this paper, we present and evaluate a rule-based NLG technique for summarising sets of bibliogr…
Empirical Optimal Risk to Quantify Model Trustworthiness for Failure Detection
Shuang Ao, Stefan Rueger, Advaith Siddharthan
Failure detection (FD) in AI systems is a crucial safeguard for the deployment for safety-critical tasks. The common evaluation method of FD performance is the Risk-coverage (RC) c…
Two Sides of Miscalibration: Identifying Over and Under-Confidence Prediction for Network Calibration
Shuang Ao, Stefan Rueger, Advaith Siddharthan
Proper confidence calibration of deep neural networks is essential for reliable predictions in safety-critical tasks. Miscalibration can lead to model over-confidence and/or under-…
Confidence-Aware Calibration and Scoring Functions for Curriculum Learning
Shuang Ao, Stefan Rueger, Advaith Siddharthan
Despite the great success of state-of-the-art deep neural networks, several studies have reported models to be over-confident in predictions, indicating miscalibration. Label Smoot…
Extractive and Abstractive Sentence Labelling of Sentiment-bearing Topics
Mohamad Hardyman Barawi, Chenghua Lin, Advaith Siddharthan +1
This paper tackles the problem of automatically labelling sentiment-bearing topics with descriptive sentence labels. We propose two approaches to the problem, one extractive and th…