Results of the summarizing process for the arXiv paper: 2303.11315v1

In recent years, large language models (LLMs) have demonstrated remarkable performance in knowledge-driven natural language processing (NLP) tasks. However, their reliance on parametric knowledge may cause them to overlook contextual cues, leading to incorrect predictions in context-sensitive NLP tasks such as knowledge acquisition. In this paper titled "Context-faithful Prompting for Large Language Models," authors Wenxuan Zhou, Sheng Zhang, Hoifung Poon, and Muhao Chen seek to assess and enhance LLMs' contextual faithfulness in two aspects: knowledge conflict and prediction with abstention. The authors propose that LLMs' faithfulness can be significantly improved using carefully designed prompting strategies. They 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. Importantly, neither technique requires additional training or data collection efforts. To test their hypothesis, the authors conduct experiments on three datasets of two standard NLP tasks: machine reading comprehension and relation extraction. The results demonstrate significant improvement in faithfulness to contexts when using these prompting strategies. Overall, this paper highlights the importance of contextual cues in NLP tasks and provides practical solutions for improving LLMs' performance in context-sensitive scenarios. The findings have implications for various applications of NLP technology such as chatbots, virtual assistants, and automated customer service systems where understanding context is crucial for providing accurate responses.

• Large language models (LLMs) have shown remarkable performance in knowledge-driven natural language processing tasks.
• However, their reliance on parametric knowledge may cause them to overlook contextual cues, leading to incorrect predictions in context-sensitive NLP tasks such as knowledge acquisition.
• The authors seek to assess and enhance LLMs' contextual faithfulness in two aspects: knowledge conflict and prediction with abstention.
• LLMs' faithfulness can be significantly improved using carefully designed prompting strategies.
• Opinion-based prompts and counterfactual demonstrations are the most effective methods for improving faithfulness to contexts.
• Neither technique requires additional training or data collection efforts.
• Experiments conducted on three datasets of two standard NLP tasks demonstrate significant improvement in faithfulness to contexts when using these prompting strategies.
• This paper highlights the importance of contextual cues in NLP tasks and provides practical solutions for improving LLMs' performance in context-sensitive scenarios.
• The findings have implications for various applications of NLP technology such as chatbots, virtual assistants, and automated customer service systems where understanding context is crucial for providing accurate responses.

Context-faithful Prompting for Large Language Models

"In recent years, large language models (LLMs) have demonstrated remarkable performance in knowledge-driven natural language processing (NLP) tasks. However, their reliance on parametric knowledge may cause them to overlook contextual cues, leading to incorrect predictions in context-sensitive NLP tasks such as knowledge acquisition."

In this paper titled "Context-faithful Prompting for Large Language Models," authors Wenxuan Zhou, Sheng Zhang, Hoifung Poon, and Muhao Chen seek to assess and enhance LLMs' contextual faithfulness in two aspects: knowledge conflict and prediction with abstention. The authors propose that LLMs' faithfulness can be significantly improved using carefully designed prompting strategies. They 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. Importantly, neither technique requires additional training or data collection efforts.

To test their hypothesis, the authors conduct experiments on three datasets of two standard NLP tasks: machine reading comprehension and relation extraction. The results demonstrate significant improvement in faithfulness to contexts when using these prompting strategies.

Overall, this paper highlights the importance of contextual cues in NLP tasks and provides practical solutions for improving LLMs' performance in context-sensitive scenarios. The findings have implications for various applications of NLP technology such as chatbots, virtual assistants, and automated customer service systems where understanding context is crucial for providing accurate responses.