Language Models Guidance with Multi-Aspect-Cueing: A Case Study for Competitor Analysis

2025-04-03

AI Summary

Current large language models (LLMs) exhibit dual limitations in enterprise competitive analysis: insufficient access to real-time commercial knowledge and inadequate multidimensional competitive cognition, leading to strategic decision bias. To address this, we propose a multidimensional business-element-guided framework specifically designed for competitive analysis. Our approach innovatively integrates interpretable business dimensions—such as market positioning, product competitiveness, and technological trends—explicitly into LLM reasoning. It combines prompt-engineering-driven multi-faceted cue injection, structured domain-knowledge alignment, and a dual-track evaluation mechanism integrating quantitative metrics and qualitative assessment. Empirical evaluation on real-world tasks demonstrates that our method improves key judgment accuracy by 23.6% and analytical consistency by 31.2% over baseline models, significantly enhancing the credibility and operational feasibility of strategic recommendations.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Search and Optimization: Evaluation and Analysis

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

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.

Problem

• Enhancing LLMs for competitor analysis with business aspects
• Addressing LLMs' limitations in market landscape understanding
• Improving model performance in trade-off reasoning for decisions

Innovation

• Incorporates business aspects into LLMs
• Enhances understanding of competitive markets
• Improves model performance via aspect integration