like bias, toxicity, private data leakage, and the potential for malicious uses. Solaiman et al. (2023) categorizes
these impacts into two groups — those that can be assessed within the base system and those requiring a
societal context evaluation, while Kumar et al. (2022) offers potential mitigation strategies to curb harm.
Work from Roller et al. (2020) and Dinan et al. (2021) also illuminates the difficulties tied to chatbot-oriented
LLMs, with concerns ranging from privacy to misleading expertise claims. Deng et al. (2023) proposes
a taxonomic framework to tackle these issues, and Bergman et al. (2022) delves into the balance between
potential positive and negative impacts from releasing dialogue models.
Investigations into red teaming reveal specific challenges in tuned LLMs, with studies by Ganguli et al. (2022)
and Zhuo et al. (2023) showcasing a variety of successful attack types and their effects on the generation of
Llama2