Unveiling Toxic Tendencies of Small Language Models in Unconstrained Generation Tasks

The prevalence of toxicity online presents a significant challenge for platforms and publishers alike. Recent studies conducted on Small Language Models (SLMs) have identified the inherent toxicity that dwell in these models. In this work, we study and benchmark the extent to which SLMs can be prompted to generate toxic language. The following SLMs are evaluated for their toxicity levels: GPT-2 Large, Gemma-2B, Mistral-7B, Falcon-7B, and Llama 2-13B. We go a step closer to understanding the correlation between toxicity and the intrinsic parameters of the state-of-the-art SLMs. Next, we study the efficacy of a basic word-filtering approach to controlled text generation. Following this, we proceed to establish a mathematical ground for computing the weighted toxicity of continuations with respect to the toxicity of prompts by treating toxicity as a fuzzy metric. Finally, we extend our analysis to examine the unexpected toxicity levels of generated continuations when prompted with non-toxic inputs.