Scaling Effects on Zero-Shot Toxicity Detection and Real-Time Inference Latency

This report synthesises findings from 6 peer-reviewed papers addressing the following research question: Does increasing model scale improve zero-shot toxicity detection accuracy on the ToxiFrench dataset, and how does this correlate with inference latency for real-time moderation. Finetuning language models on a collection of datasets phrased as instructions has been shown to improve model performance and generalization to unseen tasks. In this paper we explore instruction finetuning with a particular focus on (1) scaling the number of tasks, (2) scaling. 6 claims were extracted from source literature; 6 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.5/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research goal: Does increasing model scale improve zero-shot toxicity detection accuracy on the ToxiFrench dataset, and how does this correlate with inference latency for real-time moderation?

Autonomous literature synthesis. Automated review score: 8.5/10. Full text and citation available at Assignee Research.