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Found 7 papers, 0 papers with code
Reducing Large Language Model Bias with Emphasis on 'Restricted Industries': Automated Dataset Augmentation and Prejudice Quantification
Despite the growing capabilities of large language models, there exists concerns about the biases they develop.
Tell me the truth: A system to measure the trustworthiness of Large Language Models
And then, given a domain, how can the trustworthiness of a system be measured?
From Ambiguity to Explicitness: NLP-Assisted 5G Specification Abstraction for Formal Analysis
Our work is proof of concept for an efficient procedure in performing formal analysis for largescale complicate specification and protocol analysis, especially for 5G and nextG communications.
Information Extraction in Domain and Generic Documents: Findings from Heuristic-based and Data-driven Approaches
To fill the gap, in this study, we investigated the accuracy and generalization abilities of heuristic-based searching and data-driven to perform two IE tasks: named entity recognition (NER) and semantic role labeling (SRL) on domain-specific and generic documents with different length.
Natural Language in Requirements Engineering for Structure Inference -- An Integrative Review
This assessment was conducted to provide a foundation for future work as well as deduce insights from the stats quo.
Pheonix at SemEval-2020 Task 5: Masking the Labels Lubricates Models for Sequence Labeling
Label masking can be used as a regularization method in sequence labeling.
A computational model implementing subjectivity with the 'Room Theory'. The case of detecting Emotion from Text
Our approach is based on three components: 1. a framework/'room' representing the point of view; 2. a benchmark representing the criteria for the analysis - in this case the emotion classification, from a study of human emotions by Robert Plutchik (1980); and 3. the document to be analyzed.
