2022.findings-acl.50 (1).pdf

– Paper focuses on improving GPT3’s response to instructions
– Provides reframing principles to address GPT3’s failures

– The paper mentions GPT3’s poor performance in following task instructions.

– Guidelines for reframing instructional prompts for LMs
– Improved performance and sample complexity with reframing

– GPT3’s response to instructions improves with short, concrete prompts.
– Reframing techniques can resolve GPT3’s failures with prompting.

– Reframing improves upon few-shot and zero-shot baselines.
– Highest gains in Answer Generation, Classification, and Verification categories.

– Reframing improves upon few-shot and zero-shot baselines.
– Reframing outperforms the original raw instruction baseline.

– The paper discusses challenges in programming language models for complex tasks.
– It mentions errors in understanding instructions and generating incorrect outputs.
– Different reframing techniques are used to resolve these errors.
– Evaluation tasks like WinoGrande and QASC are used in the study.