A developer is trying to get a large language model to solve two-step arithmetic word problems. They use a few-shot prompting strategy, providing several examples. Each example consists of a word problem followed only by its final numerical answer (e.g., 'Problem: ... Answer: 15'). The model consistently fails to solve new, slightly different word problems. What is the most likely reason for the model's poor performance?
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Ch.3 Prompting - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Analysis in Bloom's Taxonomy
Cognitive Psychology
Psychology
Social Science
Empirical Science
Science
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A developer is trying to get a large language model to solve two-step arithmetic word problems. They use a few-shot prompting strategy, providing several examples. Each example consists of a word problem followed only by its final numerical answer (e.g., 'Problem: ... Answer: 15'). The model consistently fails to solve new, slightly different word problems. What is the most likely reason for the model's poor performance?
Diagnosing Prompt Insufficiency