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LLM pipeline analyzes EU regulatory consultations with traceable data

By PulseAugur Editorial · [2 sources] ·

Researchers have developed an LLM-based system to analyze large volumes of public consultation submissions, demonstrated on the European Commission's Digital Fairness Act. The pipeline processes documents, extracts topic annotations, and links each extraction to verbatim quotes from the source text. This approach ensures traceability and transparency, generating insights beyond predefined categories and offering a domain-generic solution adaptable with prompt updates. AI

IMPACT Provides a scalable, traceable method for analyzing regulatory feedback, potentially influencing policy development.

RANK_REASON The cluster contains an academic paper detailing a new methodology and system for analysis.

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

LLM pipeline analyzes EU regulatory consultations with traceable data

COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Thales Bertaglia, Haoyang Gui, Catalina Goanta, Gerasimos Spanakis ·

    Traceable by Design: An LLM Pipeline and Dashboard for EU Regulatory Consultation Analysis

    arXiv:2605.30995v1 Announce Type: cross Abstract: Public consultations generate large volumes of data in the form of stakeholder submissions that are practically unfeasible to analyse manually. We present an end-to-end LLM-based pipeline and interactive dashboard for structured t…

  2. arXiv cs.CL TIER_1 English(EN) · Gerasimos Spanakis ·

    Traceable by Design: An LLM Pipeline and Dashboard for EU Regulatory Consultation Analysis

    Public consultations generate large volumes of data in the form of stakeholder submissions that are practically unfeasible to analyse manually. We present an end-to-end LLM-based pipeline and interactive dashboard for structured topic extraction from regulatory consultation submi…

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