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This paper explores new methods for locating the sources used to write atext, by fine-tuning a variety of language models to rerank candidate sources.After retrieving candidates sources using a baseline BM25 retrieval model, avariety of reranking methods are tested to see how effective they are at thetask of source attribution. We conduct experiments on two datasets, EnglishWikipedia and medieval Arabic historical writing, and employ a variety ofretrieval and generation based reranking models. In particular, we seek tounderstand how the degree of supervision required affects the performance ofvarious reranking models. We find that semisupervised methods can be nearly aseffective as fully supervised methods while avoiding potentially costlyspan-level annotation of the target and source documents.

Citations as Queries: Source Attribution Using Language Models as Rerankers