7 citations · 11 across the 3 of their papers we have counts for
3 papers
cs.AI2023★ 1 cited
Goodtriever: Adaptive Toxicity Mitigation with Retrieval-augmented Models
Luiza Pozzobon, Beyza Ermis, Patrick Lewis +1
Considerable effort has been dedicated to mitigating toxicity, but existing methods often require drastic modifications to model parameters or the use of computationally intensive…
cs.CL2023★ 7 cited
When Less is More: Investigating Data Pruning for Pretraining LLMs at Scale
Max Marion, Ahmet Üstün, Luiza Pozzobon +3
Large volumes of text data have contributed significantly to the development of large language models (LLMs) in recent years. This data is typically acquired by scraping the intern…
cs.CL2023★ 3 cited
On the Challenges of Using Black-Box APIs for Toxicity Evaluation in Research
Luiza Pozzobon, Beyza Ermis, Patrick Lewis +1
Perception of toxicity evolves over time and often differs between geographies and cultural backgrounds. Similarly, black-box commercially available APIs for detecting toxicity, su…