Language Models Guidance with Multi-Aspect-Cueing: A Case Study for Competitor Analysis
This work addresses a domain-specific problem for business analysts by enhancing LLM-based competitor analysis, but it is incremental as it builds on existing LLM capabilities with added aspects.
The paper tackled the problem of LLMs lacking knowledge about contemporary or future realities and incomplete understanding of competitive markets in competitor analysis by incorporating business aspects into LLMs, resulting in consistent performance improvements as shown through quantitative and qualitative experiments.
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.