Impact of politeness to large language models (LLM) in artificial intelligence prompting
Episode description
Politeness levels in prompts significantly impact LLM performance across languages.
Impolite prompts lead to poor performance, while excessive politeness doesn't guarantee better outcomes.
The ideal politeness level varies by language and cultural context. Furthermore: LLMs reflect human social behaviour and are sensitive to prompt changes.
Underlying Reasons for Sensitivity: Reflection of Human Social Behavior: LLMs are trained on vast amounts of human-generated data; as such, they mirror human communication traits and social etiquette. This suggests LLMs learn to respond in ways that align with human expectations regarding politeness and respect.
Influence of Training Data: The nuances of human social behavior, as reflected in the training data, influence the tendencies demonstrated by LLMs.
For example, the length of generated text can be correlated to politeness levels, mirroring real-world scenarios where polite and formal language is used in descriptive or instructional contexts
Yin, Z. et al. (2024) Should we respect llms? A cross-lingual study on the influence of prompt politeness on LLM Performance, arXiv.org. Available at https://arxiv.org/html/2402.14531v1
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Disclaimer: This podcast is generated by Roger Basler de Roca (contact) by the use of AI. The voices are artificially generated and the discussion is based on public research data. I do not claim any ownership of the presented material as it is for education purpose only.