パラフレーズ生成と検出による言語モデル化と理解

人間が知識を共有し、世界を理由的に考え、世代を横断して生存戦略やイノベーションの方法を伝播するプロセスには、言葉の能力を超えた適応性があります。つまり、異なる単語と構造で同じ思想を表現できるのです。これこそがパラフレーズであり、意味の核となる言語モデルの強調点を握り締めています。同定や生成を行うことができる複雑な機械学習モデルも、それが持つ同一の意味を持続させたりしない、異なる意味を持つ新たなパターンについて、明示的に訓練することで結果を改善し始めるのです。

arXiv:2602.08274v1 Announce Type: cross Abstract: Language enables humans to share knowledge, reason about the world, and pass on strategies for survival and innovation across generations. At the heart of this process is not just the ability to communicate but also the remarkable flexibility in how we can express ourselves. We can express the same thoughts in virtually infinite ways using different words and structures - this ability to rephrase and reformulate expressions is known as paraphrase. Modeling paraphrases is a keystone to meaning in computational language models; being able to construct different variations of texts that convey the same meaning or not shows strong abilities of semantic understanding. If computational language models are to represent meaning, they must understand and control the different aspects that construct the same meaning as opposed to different meanings at a fine granularity. Yet most existing approaches reduce paraphrasing to a binary decision between two texts or to producing a single rewrite of a source, obscuring which linguistic factors are responsible for meaning preservation. In this thesis, I propose that decomposing paraphrases into their constituent linguistic aspects (paraphrase types) offers a more fine-grained and cognitively grounded view of semantic equivalence. I show that even advanced machine learning models struggle with this task. Yet, when explicitly trained on paraphrase types, models achieve stronger performance on related paraphrase tasks and downstream applications. For example, in plagiarism detection, language models trained on paraphrase types surpass human baselines: 89.6% accuracy compared to 78.4% for plagiarism cases from Wikipedia, and 66.5% compared to 55.7% for plagiarism of scientific papers from arXiv. In identifying duplicate questions on Quora, models trained with paraphrase types improve over models trained on binary pairs. Furthermore, I demonstrate that...