Large language models (LLMs) show impressive abilities via few-shot prompting. Commercialized APIs such as OpenAI GPT-3 further increase their use in real-world language applications. However, existing research focuses on models’ accuracy on standard benchmarks and largely ignores their reliability, which is crucial for avoiding catastrophic real-world harms. While reliability is a broad and vaguely defined term, this work decomposes reliability into four facets: generalizability, fairness, calibration, and factuality. We establish simple and effective prompts to demonstrate GPT-3’s reliability in these four aspects: 1) generalize out-of-domain, 2) balance demographic distribution to reduce social biases, 3) calibrate language model probabilities, and 4) update the LLM’s knowledge. We find that by employing appropriate prompts, GPT-3 outperforms smaller-scale supervised models by large margins on all these facets. We release all processed datasets, evaluation scripts, and model predictions to facilitate future analysis. Our findings not only shed new insights on the reliability of prompting LLMs, but more importantly, our prompting strategies can help practitioners more reliably use large language models like GPT-3.<\/p>\n","protected":false},"excerpt":{"rendered":"
Large language models (LLMs) show impressive abilities via few-shot prompting. Commercialized APIs such as OpenAI GPT-3 further increase their use in real-world language applications. However, existing research focuses on models’ accuracy on standard benchmarks and largely ignores their reliability, which is crucial for avoiding catastrophic real-world harms. While reliability is a broad and vaguely defined 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