用集成方法检测科学文本简化中的幻觉，提升生成可靠性。
Hallucination Detection and Mitigation in Scientific Text Simplification using Ensemble Approaches: DS@GT at CLEF 2025 SimpleText
- 构建多模型集成框架，融合分类、语义相似度等信号。
- 通过元分类器整合多源信息，显著提升幻觉识别准确率。
- 适合关注生成内容可信度的研究者与开发者。
本文介绍了我们在 CLEF 2025 SimpleText Task 2 中的方法，该任务聚焦于科学文本简化中创造性生成与信息失真的检测与评估。我们的解决方案整合多种策略：构建一个集成框架，利用基于 BERT 的分类器、语义相似度度量、自然语言推理模型以及大语言模型（LLM）推理。这些多元信号通过元分类器融合，增强虚假信息与内容扭曲的检测鲁棒性。此外，针对可信生成，我们采用基于 LLM 的后编辑系统，根据原始输入文本修正简化结果。
原文摘要 · Abstract (English)
In this paper, we describe our methodology for the CLEF 2025 SimpleText Task 2, which focuses on detecting and evaluating creative generation and information distortion in scientific text simplification. Our solution integrates multiple strategies: we construct an ensemble framework that leverages BERT-based classifier, semantic similarity measure, natural language inference model, and large language model (LLM) reasoning. These diverse signals are combined using meta-classifiers to enhance the robustness of spurious and distortion detection. Additionally, for grounded generation, we employ an LLM-based post-editing system that revises simplifications based on the original input texts.
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