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Found 6 papers, 2 papers with code
Gender Bias in BERT - Measuring and Analysing Biases through Sentiment Rating in a Realistic Downstream Classification Task
Pretrained language models are publicly available and constantly finetuned for various real-life applications.
Style Vectors for Steering Generative Large Language Model
This research explores strategies for steering the output of large language models (LLMs) towards specific styles, such as sentiment, emotion, or writing style, by adding style vectors to the activations of hidden layers during text generation.
Requirements for Explainability and Acceptance of Artificial Intelligence in Collaborative Work
The acceptance of AI systems depends on information about the system's functions and performance, privacy and ethical considerations, as well as goal-supporting information tailored to individual preferences and information to establish trust in the system.
Gender Bias in BERT -- Measuring and Analysing Biases through Sentiment Rating in a Realistic Downstream Classification Task
Pretrained language models are publicly available and constantly finetuned for various real-life applications.
ChatGPT is fun, but it is not funny! Humor is still challenging Large Language Models
In a series of exploratory experiments around jokes, i. e., generation, explanation, and detection, we seek to understand ChatGPT's capability to grasp and reproduce human humor.
BERT has a Moral Compass: Improvements of ethical and moral values of machines
But has BERT also a better moral compass?
