2210.09150.pdf- “experienceing the Power of Large Language Models: A Guide” – “Improving Reliability in Language Models: Strategies and Techniques” – “Harnessing the Potential of GPT-3: Tips and Tricks” – “Exploring the Four Facets of Reliability in GPT-3” – “Prompts that Enhance GPT-3’s Performance: A Deep Dive” – “Demystifying the Magic of GPT-3: Behind the Scenes” – “From Prompting to Generalizability: Unleashing GPT-3’s Potential” – “Addressing Social Biases in GPT-3: A Promising Approach” – “Calibration and Factuality: Key Aspects of Reliable Language Models” – “Maximizing the Reliability of GPT-3: Best Practices and Insights”

– Large language models (LLMs) are dominant in NLP.
– GPT-3 is a popular and flexible LLM.

– GPT-3 is a large language model (LLM) that is popular and easy to use.
– GPT-3 can be prompted with natural language text to shape predictions.
– GPT-3’s reliability can be improved through effective prompts.
– GPT-3 outperforms smaller-scale supervised models in terms of reliability.

– Provides practical recommendations for users of GPT-3.
– Inspires future work on examining more facets of reliability and applying prompting methods to real-world applications.

– The paper explores how to improve the reliability of GPT-3.
– It focuses on four facets of reliability: generalizability, social biases, calibration, and factuality.

– Effective prompting strategies improve GPT-3’s reliability.
– GPT-3 outperforms supervised models on multiple facets.

– GPT-3 is better calibrated than supervised DPR-BERT.
– Increasing the number of examples in the prompt improves accuracy.
– GPT-3 has similar calibration regardless of the source of examples.
– GPT-3’s confidence scores are more discriminative.
– Selective prediction based on GPT-3 confidence scores is effective.

– Large language models (LLMs) are powerful tools for understanding and generating text.
– GPT-3 is a popular LLM that is easy to use.
– GPT-3 can be made more reliable by using specific prompts.
– Reliability includes factors like generalizability, social biases, calibration, and factuality.
– Prompting strategies can help practitioners use GPT-3 more reliably.