用大模型构建检索重排框架，提升跨文体作者归属准确率。
Cross-Genre Authorship Attribution via LLM-Based Retrieve-and-Rerank
- 分两阶段：先检索候选作者，再用微调大模型重排
- 在两个基准上分别提升22.3和34.4的准确率
- 适合做跨文体作者识别的研究者和应用者
作者归属（AA）是从预定义作者集合中识别查询文本最可能作者的任务。本文提出一种两阶段检索-重排框架，通过微调大模型实现跨文体作者归属。与信息检索领域不同，跨文体AA需避开主题线索，专注于不依赖题材的作者语言特征。为此，我们发现传统信息检索的训练策略在跨文体场景下严重不匹配，导致性能不佳。为此，我们设计了针对性数据构建策略，使重排模型能有效学习作者区分性信号。使用该大模型检索重排流程，在HIATUS挑战性数据集的HRS1和HRS2基准上，分别比此前最优方法提升22.3和34.4的Success@8绝对分数。
原文摘要 · Abstract (English)
Authorship attribution (AA) is the task of identifying the most likely author of a query document from a predefined set of candidate authors. We introduce a two-stage retrieve-and-rerank framework that finetunes LLMs for cross-genre AA. Unlike the field of information retrieval (IR), where retrieve-and-rerank is a de facto strategy, cross-genre AA systems must avoid relying on topical cues and instead learn to identify author-specific linguistic patterns that are independent of the text's subject matter (genre/domain/topic). Consequently, for the reranker, we demonstrate that training strategies commonly used in IR are fundamentally misaligned with cross-genre AA, leading to suboptimal behavior. To address this, we introduce a targeted data curation strategy that enables the reranker to effectively learn author-discriminative signals. Using our LLM-based retrieve-and-rerank pipeline, we achieve substantial gains of 22.3 and 34.4 absolute Success@8 points over the previous state-of-the-art on HIATUS's challenging HRS1 and HRS2 cross-genre AA benchmarks.
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