Chain-of-Thought Prompting Effects on Retrieval Precision in Multiple Needles Benchmarks

Recent advancements in Large Language Models (LLMs) have marked significant progress in understanding and responding to medical inquiries. However, their performance still falls short of the standards set by professional consultations. This paper introduces a novel framework for medical consultation, comprising two main modules: Terminology-Enhanced Information Retrieval (TEIR) and Emotional In-Context Learning (EICL). TEIR ensures implicit reasoning through the utilization of inductive knowledge and key terminology retrieval, overcoming the limitations of restricted domain knowledge in public

Research goal: To what extent does chain-of-thought prompting improve retrieval precision on Multiple Needles In A Haystack benchmarks compared to direct answering strategies across varying context window lengths?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 8.0/10.