Evaluating prompting strategies and large language models in systematic literature review screening: Relevance and task-stage classification.

Abstract

This study quantifies how prompting strategies interact with large language models (LLMs) to automate the screening stage of systematic literature reviews (SLRs). We evaluate six LLMs (GPT-4o, GPT-4o-mini, DeepSeek-Chat-V3, Gemini-2.5-Flash, Claude-3.5-Haiku, and Llama-4-Maverick) under five prompt types (zero-shot, few-shot, chain-of-thought [CoT], CoT-few-shot, and self-reflection) across relevance classification and six Level-2 tasks, using accuracy, precision, recall, and F1. Results show pronounced model-prompt interaction effects within this SLR-automation corpus: zero-shot maximizes recall for high-sensitivity relevance screening; CoT-few-shot yields stronger precision and F1; and self-reflection underperforms due to over-inclusivity and instability across models. GPT-4o and DeepSeek provide robust overall performance, while GPT-4o-mini performs competitively at a substantially lower dollar cost. A cost-performance analysis for relevance classification (per 1,000 abstracts) reveals large absolute differences among model-prompt pairings; GPT-4o-Mini remains low-cost across prompts, and structured prompts (CoT/CoT-few-shot) on GPT-4o-Mini offer attractive F1 at a small incremental cost. The cost-performance analysis also suggests that future screening workflows could incorporate staged, cost-aware procedures: (1) using lower-cost, high-recall configurations for initial screening and (2) reserving higher-cost or more precision-oriented configurations for later triage or uncertain records. These findings highlight LLMs' uneven but promising potential to support screening within corpora similar to the SLR-automation corpus examined in this study. Further validation is needed before generalising these results to broader systematic review screening tasks. By systematically analyzing prompt-model interactions, we provide a comparative benchmark and practical guidance for task-adaptive LLM deployment.