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Searching the Literature by Meaning: Benchmarking Free and Commercial Word Embedding Models on Social Work Text
Brian E Perron1, Miao Wang2, Nanyi Deng3
1School of Social Work, University of Michigan, Ann Arbor, Michigan, USA.
Journal of Evidence-Based Social Work (2019)
|August 13, 2026
Summary
Free artificial intelligence (AI) embedding models can effectively search social work literature, matching or surpassing paid services. These accessible tools offer high-quality, meaning-based searches without subscriptions or external data sharing.
Area of Science:
- Information Science
- Social Work Research
- Artificial Intelligence
Background:
- Traditional literature searches rely on keyword matching, which can be limiting.
- Commercial AI embedding models offer meaning-based search but come with costs.
- The need for accessible, high-quality search tools in social work is significant.
Purpose of the Study:
- To evaluate free embedding models for searching social work literature.
- To compare the performance of free models against commercial standards.
- To determine the necessity and impact of rerankers in search.
Main Methods:
- Tested twelve free and two commercial AI embedding models on 64,956 social work records.
- Utilized 150 curated queries and conducted over 50,000 blind head-to-head comparisons judged by AI.
- Validated results using a judge-free known-item test and expert human evaluation.
Main Results:
- Free embedding models matched or exceeded the performance of commercial options.
- One free model, with a reranker, achieved the best overall search configuration.
- Keyword search significantly underperformed all tested embedding models.
Conclusions:
- High-quality, meaning-based literature search is feasible using free AI tools on standard hardware.
- The choice of embedding model should consider factors like size, speed, cost, and privacy.
- Accessible AI-powered search democratizes evidence-based practice in social work.
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