Search Results for author:
Found 7 papers, 6 papers with code
To Know or Not To Know? Analyzing Self-Consistency of Large Language Models under Ambiguity
This paper focuses on entity type ambiguity, analyzing the proficiency and consistency of state-of-the-art LLMs in applying factual knowledge when prompted with ambiguous entities.
Analysing zero-shot temporal relation extraction on clinical notes using temporal consistency
Our experiments demonstrate that LLMs struggle in the zero-shot setting performing worse than fine-tuned specialized models in terms of F1 score, showing that this is a challenging task for LLMs.
Exploring prompts to elicit memorization in masked language model-based named entity recognition
Finally, the prompt performance of detecting model memorization is quantified by the percentage of name pairs for which the model has higher confidence for the name from the training set.
Learning with Noisy Labels by Adaptive Gradient-Based Outlier Removal
Instead of cleaning the dataset prior to model training, the dataset is dynamically adjusted during the training process.
ACTC: Active Threshold Calibration for Cold-Start Knowledge Graph Completion
In this paper, we attempt for the first time cold-start calibration for KGC, where no annotated examples exist initially for calibration, and only a limited number of tuples can be selected for annotation.
ULF: Unsupervised Labeling Function Correction using Cross-Validation for Weak Supervision
A cost-effective alternative to manual data labeling is weak supervision (WS), where data samples are automatically annotated using a predefined set of labeling functions (LFs), rule-based mechanisms that generate artificial labels for the associated classes.
Knodle: Modular Weakly Supervised Learning with PyTorch
Strategies for improving the training and prediction quality of weakly supervised machine learning models vary in how much they are tailored to a specific task or integrated with a specific model architecture.
