The paper addresses the challenge of traditional technology mapping methods struggling with the vast amounts of unstructured data and emerging technologies. Keyword-based approaches are often inflexible and domain-specific.
This paper introduces STARS, a framework combining LLM-based entity extraction with Sentence-BERT semantic ranking. STARS identifies technologies in unstructured content, builds company profiles, and ranks technologies by operational importance.
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https://arxiv.org/abs/2501.15120
📌 Chain-of-Thought prompting in STARS effectively guides LLMs for enhanced technology extraction. This step improves over simpler prompting methods by mimicking a more reasoned approach.
📌 STARS leverages Sentence-BERT's semantic embeddings for ranking, which is crucial. Using Sentence-BERT directly addresses the limitation of LLMs in technology ranking by providing nuanced similarity scores.
📌 The integration of LLMs and Sentence-BERT in STARS offers a practical solution. This combination effectively tackles technology mapping by combining extraction with semantic relevance ranking.
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Methods Explored in this Paper 🔧:
→ The paper proposes STARS, a Semantic Technology and Retrieval System. STARS uses LLMs for entity extraction and Sentence-BERT for semantic ranking.
→ STARS employs Chain-of-Thought prompting to guide the LLM in extracting relevant technologies from unstructured text. This method mimics human-like reasoning to improve entity identification.
→ Sentence-BERT is used to create embeddings for both companies and technologies. Cosine similarity between these embeddings ranks technology relevance to companies. This semantic ranking enhances precision over keyword-based methods.
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Key Insights 💡:
→ Combining LLMs with Chain-of-Thought prompting improves technology entity extraction from unstructured data.
→ Semantic ranking with Sentence-BERT enhances the accuracy of matching technologies to companies by capturing contextual relevance.
→ STARS offers a scalable and precise method for technology mapping across diverse industries, overcoming limitations of traditional approaches.
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Results 📊:
→ STARS achieves 0.762 Precision at 3 (P@3) for company-to-technology retrieval, outperforming Chain-of-Thought prompting by 14.2%.
→ STARS achieves 0.725 P@3 for technology-to-company retrieval, showing a 15.5% improvement over Chain-of-Thought prompting.
→ Using five few-shot examples in prompting improves P@3 precision from 0.667 to 0.762 for company-to-technology retrieval.
→ STARS using Sentence-BERT ranking achieves 0.762 P@3, outperforming OpenAI ranking (0.604 P@3) and TF-IDF ranking (0.561 P@3).
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