Language Models Guidance with Multi-Aspect-Cueing: A Case Study for Competitor Analysis
Fuente:
arXiv
Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2025
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866912308087750656 |
|---|---|
| author | Hadifar, Amir Ochs, Christopher Van Ewijk, Arjan |
| author_facet | Hadifar, Amir Ochs, Christopher Van Ewijk, Arjan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_02984 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Language Models Guidance with Multi-Aspect-Cueing: A Case Study for Competitor Analysis Hadifar, Amir Ochs, Christopher Van Ewijk, Arjan Artificial Intelligence Computation and Language 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. |
| title | Language Models Guidance with Multi-Aspect-Cueing: A Case Study for Competitor Analysis |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2504.02984 |