arXiv:2607.05968v1 Announce Type: cross Abstract: Matching influencers (KOLs) to free-form, multi-part Thai marketing criteria is today served either by keyword search over structured profiles, which misses semantic fit, or by prompting frontier LLMs over every candidate, which is accurate but slow and expensive. We present InfluMatch, a low-cost three-stage cascade -- retrieval $\rightarrow$ rerank $\rightarrow$ reason -- built entirely from small open-weight models: dense retrieval returns 50 candidates, a 4B pointwise reranker scores each by the log-probability of a single Yes token and kee
The proliferation of open-weight, smaller language models and the increasing demand for cost-effective, high-performance AI solutions are driving innovations in application-specific AI architectures.
This development demonstrates a significant advancement in achieving frontier-quality AI outcomes with substantially reduced computational resources and costs, democratizing access to complex AI capabilities.
The ability to run advanced influencer matching, a sophisticated AI task, at a 4B-model cost changes the economic landscape for AI-powered marketing and similar applications, making them accessible to a broader range of businesses.
- · SME marketing agencies
- · Open-source AI model developers
- · Businesses in emerging markets
- · Advertisers seeking cost-effective KOL solutions
- · Providers of expensive frontier LLM APIs
- · Traditional keyword-based matching platforms
- · Marketing firms reliant on high-cost AI infrastructure
Wider adoption of sophisticated AI matching systems due to lower operational costs.
Increased competition in AI-powered marketing and a shift towards 'cascading' AI architectures for efficiency.
Potential for similar cost-effective, small-model solutions to emerge in other white-collar automation domains, accelerating AI agent development.
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Read at arXiv cs.AI