This article explores the optimal placement of few-shot examples when tuning the Jev LLM for specific tasks. The author tested four different methods for incorporating these examples: within the model's state as 'worked_examples', directly in the instructions, embedded within criteria definitions, or flattened at the same level as the target case. For risk-related tasks, the 'flattened state' approach showed a favorable, though not statistically significant, improvement over baseline methods. AI
IMPACT Investigating optimal few-shot example placement could lead to more efficient and accurate LLM tuning for specialized tasks.
RANK_REASON The article details an experimental investigation into optimizing LLM performance through specific data placement techniques, which falls under research. [lever_c_demoted from research: ic=1 ai=1.0]
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