Search Results for author:
Found 9 papers, 5 papers with code
Comparing Specialised Small and General Large Language Models on Text Classification: 100 Labelled Samples to Achieve Break-Even Performance
When performance variance is taken into consideration, the number of required labels increases on average by $100 - 200\%$ and even up to $1500\%$ in specific cases.
On Sensitivity of Learning with Limited Labelled Data to the Effects of Randomness: Impact of Interactions and Systematic Choices
To measure the true effects of an individual randomness factor, our method mitigates the effects of other factors and observes how the performance varies across multiple runs.
Automatic Combination of Sample Selection Strategies for Few-Shot Learning
In few-shot learning, such as meta-learning, few-shot fine-tuning or in-context learning, the limited number of samples used to train a model have a significant impact on the overall success.
Effects of diversity incentives on sample diversity and downstream model performance in LLM-based text augmentation
The latest generative large language models (LLMs) have found their application in data augmentation tasks, where small numbers of text samples are LLM-paraphrased and then used to fine-tune downstream models.
On the Effects of Randomness on Stability of Learning with Limited Labelled Data: A Systematic Literature Review
Recently, this area started to attract a research attention and the number of relevant studies is continuously growing.
KInITVeraAI at SemEval-2023 Task 3: Simple yet Powerful Multilingual Fine-Tuning for Persuasion Techniques Detection
This paper presents the best-performing solution to the SemEval 2023 Task 3 on the subtask 3 dedicated to persuasion techniques detection.
Auditing YouTube's Recommendation Algorithm for Misinformation Filter Bubbles
We also observe a sudden decrease of misinformation filter bubble effect when misinformation debunking videos are watched after misinformation promoting videos, suggesting a strong contextuality of recommendations.
Monant Medical Misinformation Dataset: Mapping Articles to Fact-Checked Claims
It also contains 573 manually and more than 51k automatically labelled mappings between claims and articles.
An Audit of Misinformation Filter Bubbles on YouTube: Bubble Bursting and Recent Behavior Changes
We present a study in which pre-programmed agents (acting as YouTube users) delve into misinformation filter bubbles by watching misinformation promoting content (for various topics).
