Passage Re-Ranking
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Benchmarks
Most implemented
Passage Re-ranking with BERT
Document Expansion by Query Prediction
Rank-DistiLLM: Closing the Effectiveness Gap Between Cross-Encoders and LLMs for Passage Re-Ranking
Set-Encoder: Permutation-Invariant Inter-Passage Attention for Listwise Passage Re-Ranking with Cross-Encoders
Few-shot Reranking for Multi-hop QA via Language Model Prompting
Dealing with Typos for BERT-based Passage Retrieval and Ranking
Papers
From Interpretability to Performance: Optimizing Retrieval Heads for Long-Context Language Models
Advances in mechanistic interpretability have identified special attention heads, known as retrieval heads, that are responsible for retrieving information from the context. However, the role of these retrieval heads in …
Passage Re-RankingExploring the Effectiveness of Multi-stage Fine-tuning for Cross-encoder Re-rankers
State-of-the-art cross-encoders can be fine-tuned to be highly effective in passage re-ranking. The typical fine-tuning process of cross-encoders as re-rankers requires large amounts of manually labelled data, a contrast…
Contrastive LearningLanguage ModelingLanguage ModellingLarge Language Model+2Re-Ranking Step by Step: Investigating Pre-Filtering for Re-Ranking with Large Language Models
Large Language Models (LLMs) have been revolutionizing a myriad of natural language processing tasks with their diverse zero-shot capabilities. Indeed, existing work has shown that LLMs can be used to great effect for ma…
Information RetrievalPassage RankingPassage Re-RankingRe-RankingEfficient Document Ranking with Learnable Late Interactions
Cross-Encoder (CE) and Dual-Encoder (DE) models are two fundamental approaches for query-document relevance in information retrieval. To predict relevance, CE models use joint query-document embeddings, while DE models m…
Document RankingInformation RetrievalPassage Re-RankingRe-RankingRank-DistiLLM: Closing the Effectiveness Gap Between Cross-Encoders and LLMs for Passage Re-Ranking
Cross-encoders distilled from large language models (LLMs) are often more effective re-rankers than cross-encoders fine-tuned on manually labeled data. However, distilled models do not match the effectiveness of their te…
Language ModellingLarge Language ModelPassage Re-RankingRe-RankingSet-Encoder: Permutation-Invariant Inter-Passage Attention for Listwise Passage Re-Ranking with Cross-Encoders
Existing cross-encoder models can be categorized as pointwise, pairwise, or listwise. Pairwise and listwise models allow passage interactions, which typically makes them more effective than pointwise models but less effi…
Passage Re-RankingRe-Ranking