Papers by Thomas Berkane

    2 papers
    LLM-Based Web Data Collection for Research Dataset Creation (2025.findings-emnlp)

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    Challenge: researchers across many fields rely on web data to gain new insights and validate methods.
    Approach: They propose a human-in-the-loop framework that automates web-scale data collection end-to-end using large language models (LLMs)
    Outcome: The proposed framework outperforms existing methods in three different tasks and a user evaluation demonstrates its practical utility.
    The AI Committee: A Multi-Agent Framework for Automated Validation and Remediation of Web-Sourced Data (2026.eacl-demo)

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    Challenge: largelanguage models (LLMs)-powered web agents can be useful for research in areas such as social science, public health, and economics.
    Approach: They propose a model-agnostic multi-agent system that auto-mates the process of validating and remediatingweb-sourced datasets.
    Outcome: The proposed system outperforms baseline approaches and achieves datacompleteness and precision up to 73.3%.

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