FFT: Towards Harmlessness Evaluation and Analysis for LLMs with
  Factuality, Fairness, Toxicity
Shiyao Cui Zhenyu Zhang Yilong Chen Wenyuan Zhang Tianyun Liu Siqi Wang Tingwen Liu
The widespread of generative artificial intelligence has heightened concerns
about the potential harms posed by AI-generated texts, primarily stemming from
factoid, unfair, and toxic content. Previous researchers have invested much
effort in assessing the harmlessness of generative language models. However,
existing benchmarks are struggling in the era of large language models (LLMs),
due to the stronger language generation and instruction following capabilities,
as well as wider applications. In this paper, we propose FFT, a new benchmark
with 2116 elaborated-designed instances, for LLM harmlessness evaluation with
factuality, fairness, and toxicity. To investigate the potential harms of LLMs,
we evaluate 9 representative LLMs covering various parameter scales, training
stages, and creators. Experiments show that the harmlessness of LLMs is still
under-satisfactory, and extensive analysis derives some insightful findings
that could inspire future research for harmless LLM research.