用多维度商业线索引导大模型，提升竞品分析能力
Language Models Guidance with Multi-Aspect-Cueing: A Case Study for Competitor Analysis
- 将商业维度信息注入大模型，增强对竞争格局的理解
- 实验证明多维度引导可稳定提升模型分析性能
- 适合需要精准竞品洞察的商业决策者使用
竞品分析在现代商业中至关重要，需综合评估多个方面并权衡取舍以做出明智决策。近年来的大语言模型（LLMs）虽展现出处理此类权衡的推理能力，但仍受限于对当前或未来现实的认知不足，以及对市场竞争力格局理解不完整。本文通过将业务维度信息融入大模型，弥补这一差距。定量与定性实验表明，引入这些维度能持续提升模型表现，从而增强竞品分析的准确性与有效性。
原文摘要 · Abstract (English)
Competitor analysis is essential in modern business due to the influence of industry rivals on strategic planning. It involves assessing multiple aspects and balancing trade-offs to make informed decisions. Recent Large Language Models (LLMs) have demonstrated impressive capabilities to reason about such trade-offs but grapple with inherent limitations such as a lack of knowledge about contemporary or future realities and an incomplete understanding of a market's competitive landscape. In this paper, we address this gap by incorporating business aspects into LLMs to enhance their understanding of a competitive market. Through quantitative and qualitative experiments, we illustrate how integrating such aspects consistently improves model performance, thereby enhancing analytical efficacy in competitor analysis.
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