DynRank conditions UPR-style passage reranking on an automatically inferred fine-grained question type and reports small gains over static prompting on NQ, TriviaQA, WebQuestions, and BEIR.
Investigating the Successes and Failures of BERT for Passage Re-Ranking
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abstract
The bidirectional encoder representations from transformers (BERT) model has recently advanced the state-of-the-art in passage re-ranking. In this paper, we analyze the results produced by a fine-tuned BERT model to better understand the reasons behind such substantial improvements. To this aim, we focus on the MS MARCO passage re-ranking dataset and provide potential reasons for the successes and failures of BERT for retrieval. In more detail, we empirically study a set of hypotheses and provide additional analysis to explain the successful performance of BERT.
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DynRank: Improving Passage Retrieval with Dynamic Zero-Shot Prompting Based on Question Classification
DynRank conditions UPR-style passage reranking on an automatically inferred fine-grained question type and reports small gains over static prompting on NQ, TriviaQA, WebQuestions, and BEIR.