  Recent studies have demonstrated that large language models (LLMs) excel in
diverse tasks through in-context learning (ICL) facilitated by task-specific
prompts and examples. However, the existing literature shows that ICL
encounters performance deterioration when exposed to adversarial inputs.
Enhanced performance has been observed when ICL is augmented with natural
language explanations (NLEs) (we refer to it as X-ICL). Thus, this work
investigates whether X-ICL can improve the robustness of LLMs on a suite of
seven adversarial and challenging natural language inference datasets.
Moreover, we introduce a new approach to X-ICL by prompting an LLM (ChatGPT in
our case) with few human-generated NLEs to produce further NLEs (we call it
ChatGPT few-shot), which we show superior to both ChatGPT zero-shot and
human-generated NLEs alone. We evaluate five popular LLMs (GPT3.5-turbo,
LLaMa2, Vicuna, Zephyr, Mistral) and show that X-ICL with ChatGPT few-shot
yields over 6% improvement over ICL. Furthermore, while prompt selection
strategies were previously shown to significantly improve ICL on
in-distribution test sets, we show that these strategies do not match the
efficacy of the X-ICL paradigm in robustness-oriented evaluations.

Computation and Language