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Found 8 papers, 3 papers with code
Bias Discovery within Human Raters: A Case Study of the Jigsaw Dataset
Understanding and quantifying the bias introduced by human annotation of data is a crucial problem for trustworthy supervised learning.
Perspectives in Play: A Multi-Perspective Approach for More Inclusive NLP Systems
In the realm of Natural Language Processing (NLP), common approaches for handling human disagreement consist of aggregating annotators' viewpoints to establish a single ground truth.
Embracing Diversity: A Multi-Perspective Approach with Soft Labels
Prior studies show that adopting the annotation diversity shaped by different backgrounds and life experiences and incorporating them into the model learning, i. e. multi-perspective approach, contribute to the development of more responsible models.
All-in-one: Understanding and Generation in Multimodal Reasoning with the MAIA Benchmark
We introduce MAIA (Multimodal AI Assessment), a native-Italian benchmark designed for fine-grained investigation of the reasoning abilities of visual language models on videos.
Multi-Perspective Stance Detection
Subjective NLP tasks usually rely on human annotations provided by multiple annotators, whose judgments may vary due to their diverse backgrounds and life experiences.
Prompting Encoder Models for Zero-Shot Classification: A Cross-Domain Study in Italian
Our study concentrates on the Italian bureaucratic and legal language, experimenting with both general-purpose and further pre-trained encoder-only models.
Continually Learn to Map Visual Concepts to Large Language Models in Resource-constrained Environments
This work proposes Continual Visual Mapping (CVM), an approach that continually ground vision representations to a knowledge space extracted from a fixed Language model.
Continual Pre-Training Mitigates Forgetting in Language and Vision
We formalize and investigate the characteristics of the continual pre-training scenario in both language and vision environments, where a model is continually pre-trained on a stream of incoming data and only later fine-tuned to different downstream tasks.
