REVIEW 4 cited by
Should We Respect LLMs? A Cross-Lingual Study on the Influence of Prompt Politeness on LLM Performance
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
We investigate the impact of politeness levels in prompts on the performance of large language models (LLMs). Polite language in human communications often garners more compliance and effectiveness, while rudeness can cause aversion, impacting response quality. We consider that LLMs mirror human communication traits, suggesting they align with human cultural norms. We assess the impact of politeness in prompts on LLMs across English, Chinese, and Japanese tasks. We observed that impolite prompts often result in poor performance, but overly polite language does not guarantee better outcomes. The best politeness level is different according to the language. This phenomenon suggests that LLMs not only reflect human behavior but are also influenced by language, particularly in different cultural contexts. Our findings highlight the need to factor in politeness for cross-cultural natural language processing and LLM usage.
Forward citations
Cited by 4 Pith papers
-
A Japanese Language Model and Three New Evaluation Benchmarks for Pharmaceutical NLP
A continually pretrained 7B Japanese pharmaceutical LLM outperforms open medical models on new Japanese pharma benchmarks, while all models, including GPT-4o, fail at cross-sentence consistency checks.
-
Expect the Unexpected: FailSafe Long Context QA for Finance
FailSafeQA, a 220-example financial long-context benchmark, shows no tested LLM can both stay robust to input perturbations and refuse to hallucinate when context is missing or irrelevant.
-
LLMs for LLMs: A Structured Prompting Methodology for Long Legal Documents
On CUAD legal contracts, a prompt-engineered QWEN-2 pipeline with chunking and two answer-selection heuristics reportedly outperforms the fine-tuned DeBERTa-large baseline by about 9%, reaching claimed state-of-the-ar...
-
Boosting Self-Efficacy and Performance of Large Language Models via Verbal Efficacy Stimulations
Emotionally styled verbal prompts (encouraging, provocative, critical) modestly improve zero-shot LLM accuracy on many tasks, with the best style varying by model and task zone.
Discussion (0). Continue with ORCID to comment.