mlsanityData to 14 Oct 2026

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

arXiv 2504.02984 ↗First posted 3 Apr 2025cs.AI cs.CLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 30 references that are in the map. First posted on arXiv 3 Apr 2025.

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References · 11

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
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  3. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  4. 2022Large Language Models are Zero-Shot Reasonerscited by 4,374
  5. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,592
  6. 2022GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformerscited by 1,919
  7. 2023BloombergGPT: A Large Language Model for Financecited by 939
  8. 2019Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approachcited by 308
  9. 2024Knowledge Conflicts for LLMs: A Surveycited by 283
  10. 2023Large language models can accurately predict searcher preferencescited by 131
  11. 2023CompanyKG: A Large-Scale Heterogeneous Graph for Company Similarity Quantificationcited by 6

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