The content discusses the creation of PARADISE, an abductive reasoning task using procedural text from wikiHow to evaluate language models' planning skills. The study highlights the effectiveness of task-specific small models over large language models in most scenarios. Despite advancements, all models fall short of human performance. The analysis uncovers intriguing insights such as variations in model behavior with dropped keywords and struggles with different types of goals.
The dataset contains warnings and tips associated with goals, excluding intermediary steps, to test the ability of models to infer implicit knowledge solely from the given goal. Experiments reveal that fine-tuned small models tailored to specific tasks are more effective than zero-shot prompting across all LLMs, including GPT-4. However, all models still lag behind human performance.
The study also delves into research questions such as whether models perform well due to simple keyword matching, if different model families fail on different instances, and how performance compares for explicit versus implicit warnings/tips. Additionally, it explores reverse inference tasks and the transfer learning impact of proposed tasks on out-of-domain procedural tasks.
To Another Language
from source content
arxiv.org
Deeper Inquiries