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What Happens When You Tell an LLM It Has an iPhone Next to It?
- The article discusses the influence of environmental cues on human cognitive performance and language model behavior.
- The author conducted an experiment to test if telling a language model about an environmental condition, like having a phone nearby, would impact its performance.
- Results showed that while the presence of a phone impaired human performance, suggesting it to the language model improved its performance.
- An open-source script named EvaluateGPT was used to evaluate the effectiveness of the language model prompt in generating SQL queries.
- Findings revealed that adding a sentence in a lengthy system prompt significantly enhanced the accuracy of the Gemini Flash 2 model in generating SQL queries.
- The study suggests potential practical applications in improving the accuracy of language models by understanding their thought processes.
- The importance of diversity of thought is highlighted in the author's nontraditional approach combining psychology, AI, and software engineering skills.
- However, the article cautions that the results are specific to the Gemini Flash 2.0 model and SQL query generation, and may not apply universally to all language models or tasks.
- The limitations of the study include the small sample size of 20 questions and the need for more robust experimentation for generalizability.
- The author is open to expanding the research and potentially publishing a more comprehensive paper based on the traction this study receives.
- For transparency, the author has shared the full output, system prompts, and evaluations on Google Drive, along with releasing EvaluateGPT for evaluating LLM output effectiveness.
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