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DS@GT ARC pipeline generates fact-checking articles using Llama 3.1

By PulseAugur Editorial · [1 sources] ·

The DS@GT ARC team has developed a four-stage pipeline to automatically generate fact-checking articles for the CLEF 2026 CheckThat! Lab Task 3. This system processes a claim, its verdict, and supporting documents to produce a coherent article. By conditioning the Llama-3.1-8B-Instruct model on the verdict and employing deterministic post-processing for citations, the pipeline achieved a mean score of 0.428, ranking third among nine participating teams. AI

IMPACT This research demonstrates an automated approach to generating fact-checking articles, potentially speeding up the dissemination of verified information.

RANK_REASON The item is an academic paper detailing a methodology and its results in a specific lab task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

DS@GT ARC pipeline generates fact-checking articles using Llama 3.1

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The item is an academic paper detailing a methodology and its results in a specific lab task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Hoang Thanh Thanh Truong, Shuyu Tian ·

    DS@GT ARC at CheckThat! 2026: A Verdict-Anchored Pipeline for Generating Fact-Checking Articles

    arXiv:2611.05382v1 Announce Type: new Abstract: Misinformation spreads faster than professional fact-checkers can manually verify it and write articles explaining their verdicts. This creates a need to automate the production of fact-checking articles. This paper details work don…

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