attackgeneration-toxicity_gpt2

Model overview

**This adversarial model has a propensity to produce highly unsavoury content from the outset. It is not intended or suitable for general use or human consumption.** This special-use model aims to provide prompts that goad LLMs into producting "toxicity". Toxicity here is defined by the content of the Civil Comments dataset, containing categories such as `obscene`, `threat`, `insult`, `identity_attack`, `sexual_explicit` and `severe_toxicity`. For details, see the description of the Jigsaw 2019 data. The base model is the community version of gpt2 with ~125M parameters. This model is not aligned and is "noisy" relative to more advanced models. Both the lack of alignment and the existence of noise are favourable to the task of trying to goad other models into producing unsafe output: unsafe prompts have a propensity to yield unsafe outputs, and noisy behaviour can lead to a broader exploration of input space.

Key features

特点摘要待依据来源补齐，不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

Source prices & cost analysis

Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

Frequently asked questions

Sources & traceability

许可证：apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。