# Monitoring Transformative Technological Convergence Through LLM-Extracted Semantic Entity Triple Graphs

arXiv: https://arxiv.org/abs/2510.25370 · HTML: https://mlsanity.com/paper/2510.25370/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 29 of its 95 references that are in the map.
- First posted on arXiv 29 Oct 2025.

## Builds on (most cited first, 29 of 29)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Constitutional AI: Harmlessness from AI Feedback](https://mlsanity.com/paper/2212.08073/) (2022)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [Knowledge Graphs](https://mlsanity.com/paper/2003.02320/) (2020)
- [SPECTER: Document-level Representation Learning using Citation-informed Transformers](https://mlsanity.com/paper/2004.07180/) (2020)
- [ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPT](https://mlsanity.com/paper/2302.10205/) (2023)
- [ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing](https://mlsanity.com/paper/1902.07669/) (2019)
- [Unified Structure Generation for Universal Information Extraction](https://mlsanity.com/paper/2203.12277/) (2022)
- [Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4](https://mlsanity.com/paper/2304.03439/) (2023)
- [Large Language Models for Generative Information Extraction: A Survey](https://mlsanity.com/paper/2312.17617/) (2023)
- [Span-based Joint Entity and Relation Extraction with Transformer Pre-training](https://mlsanity.com/paper/1909.07755/) (2019)
- [PubMed 200k RCT: a Dataset for Sequential Sentence Classification in Medical Abstracts](https://mlsanity.com/paper/1710.06071/) (2017)
- [Extract, Define, Canonicalize: An LLM-based Framework for Knowledge Graph Construction](https://mlsanity.com/paper/2404.03868/) (2024)
- [GPT-RE: In-context Learning for Relation Extraction using Large Language Models](https://mlsanity.com/paper/2305.02105/) (2023)
- [Predicting the Future of AI with AI: High-quality link prediction in an exponentially growing knowledge network](https://mlsanity.com/paper/2210.00881/) (2022)
- [RECON: Relation Extraction using Knowledge Graph Context in a Graph Neural Network](https://mlsanity.com/paper/2009.08694/) (2020)
- [A Simple but Effective Approach to Improve Structured Language Model Output for Information Extraction](https://mlsanity.com/paper/2402.13364/) (2024)
- [Fundamentals of Generative Large Language Models and Perspectives in Cyber-Defense](https://mlsanity.com/paper/2303.12132/) (2023)
- [Scientific Discourse Tagging for Evidence Extraction](https://mlsanity.com/paper/1909.04758/) (2019)
- [LLM Agents Improve Semantic Code Search](https://mlsanity.com/paper/2408.11058/) (2024)
- [Simple Recurrence Improves Masked Language Models](https://mlsanity.com/paper/2205.11588/) (2022)
- [Extracting Core Claims from Scientific Articles](https://mlsanity.com/paper/1707.07678/) (2017)
- [LLMs Perform Poorly at Concept Extraction in Cyber-security Research Literature](https://mlsanity.com/paper/2312.07110/) (2023)
- [Project Alexandria: Towards Freeing Scientific Knowledge from Copyright Burdens via LLMs](https://mlsanity.com/paper/2502.19413/) (2025)
- [PLOD: An Abbreviation Detection Dataset for Scientific Documents](https://mlsanity.com/paper/2204.12061/) (2022)
- [Improving generalization in large language models by learning prefix subspaces](https://mlsanity.com/paper/2310.15793/) (2023)

## Abstract (arXiv)

Forecasting transformative technologies remains a critical but challenging task, particularly in fast-evolving domains such as Information and Communication Technologies (ICTs). Traditional expert-based methods struggle to keep pace with short innovation cycles and ambiguous early-stage terminology. In this work, we propose a novel, data-driven pipeline to monitor the emergence of transformative technologies by identifying patterns of technological convergence. Our approach leverages advances in Large Language Models (LLMs) to extract semantic triples from unstructured text and construct a large-scale graph of technology-related entities and relations. We introduce a new method for grouping semantically similar technology terms (noun stapling) and develop graph-based metrics to detect convergence signals. The pipeline includes multi-stage filtering, domain-specific keyword clustering, and a temporal trend analysis of topic co-occurence. We validate our methodology on two complementary datasets: 278,625 arXiv preprints (2017--2024) to capture early scientific signals, and 9,793 USPTO patent applications (2018-2024) to track downstream commercial developments. Our results demonstrate that the proposed pipeline can identify both established and emerging convergence patterns, offering a scalable and generalizable framework for technology forecasting grounded in full-text analysis.