用大模型当裁判自动构建简化数据，无需人工标注
Policy-based Sentence Simplification: Replacing Parallel Corpora with LLM-as-a-Judge
- 让大模型充当评判员，自动生成符合特定简化策略的数据
- 小模型Phi-3-mini在词级简化上超越GPT-4o，整体效果相当
- 适合需要灵活控制简化程度的场景，如教育、无障碍阅读
句子简化旨在修改句子使其更易读易懂，同时保持原意。不同应用场景需不同简化策略，如仅替换复杂词汇（词法层面）或重写整句以牺牲细节换取简洁性。然而，实现这种策略驱动的控制仍是开放挑战。本文提出一种简单但高效的方法，利用大语言模型作为评判员（LLM-as-a-Judge）自动构建与策略对齐的训练数据，完全避免了昂贵的人工标注或平行语料库。该方法可支持构建适应多种简化策略的系统。令人惊讶的是，即使小型开源模型如Phi-3-mini-3.8B，在词法导向的简化任务中也优于GPT-4o，整体重写性能与之相当，经自动指标和人工评估验证。跨模型家族与规模的一致提升，证明了方法的鲁棒性。
原文摘要 · Abstract (English)
Sentence simplification aims to modify a sentence to make it easier to read and understand while preserving the meaning. Different applications require distinct simplification policies, such as replacing only complex words at the lexical level or rewriting the entire sentence while trading off details for simplicity. However, achieving such policy-driven control remains an open challenge. In this work, we introduce a simple yet powerful approach that leverages Large Language Model-as-a-Judge (LLM-as-a-Judge) to automatically construct policy-aligned training data, completely removing the need for costly human annotation or parallel corpora. Our method enables building simplification systems that adapt to diverse simplification policies. Remarkably, even small-scale open-source LLMs such as Phi-3-mini-3.8B surpass GPT-4o on lexical-oriented simplification, while achieving comparable performance on overall rewriting, as verified by both automatic metrics and human evaluations. The consistent improvements across model families and sizes demonstrate the robustness of our approach.
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