Large language models (LLMs) encode parametric knowledge about world facts and have shown remarkable performance in knowledge-driven NLP tasks. However, their reliance on parametric knowledge may cause them to overlook contextual cues, leading to incorrect predictions in context-sensitive NLP tasks (e.g., knowledge acquisition tasks). In this paper, we seek to assess and enhance LLMs' contextual faithfulness in two aspects: knowledge conflict and prediction with abstention. We demonstrate that LLMs' faithfulness can be significantly improved using carefully designed prompting strategies. In particular, we identify opinion-based prompts and counterfactual demonstrations as the most effective methods. Opinion-based prompts reframe the context as a narrator's statement and inquire about the narrator's opinions, while counterfactual demonstrations use instances containing false facts to improve faithfulness in knowledge conflict situations. Neither technique requires additional training. We conduct experiments on three datasets of two standard NLP tasks, machine reading comprehension and relation extraction, and the results demonstrate significant improvement in faithfulness to contexts.

翻译：大语言模型（LLMs）编码了关于世界事实的参数化知识，并在知识驱动的自然语言处理任务中展现出卓越性能。然而，它们对参数化知识的依赖可能导致其忽略上下文线索，从而在上下文敏感型自然语言处理任务（例如知识获取任务）中产生错误预测。本文旨在从知识冲突和含弃权预测两个维度评估并提升大语言模型的上下文忠实度。我们证明，通过精心设计的提示策略可显著提升大语言模型的忠实度。特别地，我们识别出基于观点的提示和反事实示例是最有效的方法。基于观点的提示将上下文重构为叙述者的陈述并询问叙述者的观点，而反事实示例则利用包含虚假事实的实例来改善知识冲突场景下的忠实度。这两种技术均无需额外训练。我们在三个数据集上开展了机器阅读理解与关系抽取两项标准自然语言处理任务的实验，结果证实了对上下文忠实度的显著提升。