Papers Passage Re-Ranking
“Passage Re-Ranking” 태그가 달린 논문 33편 · 필터 해제
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-RankingMulti-Granularity Guided Fusion-in-Decoder
In Open-domain Question Answering (ODQA), it is essential to discern relevant contexts as evidence and avoid spurious ones among retrieved results. The model architecture that uses concatenated multiple contexts in the d…
DecoderMulti-Task LearningNatural QuestionsOpen-Domain Question Answering+6PaRaDe: Passage Ranking using Demonstrations with Large Language Models
Recent studies show that large language models (LLMs) can be instructed to effectively perform zero-shot passage re-ranking, in which the results of a first stage retrieval method, such as BM25, are rated and reordered t…
Passage RankingPassage Re-RankingQuestion GenerationQuestion-Generation+4Adapting Language Models to Compress Contexts
Transformer-based language models (LMs) are powerful and widely-applicable tools, but their usefulness is constrained by a finite context window and the expensive computational cost of processing long text documents. We …
In-Context LearningLanguage ModelingLanguage ModellingPassage Re-Ranking+2Improving Conversational Passage Re-ranking with View Ensemble
This paper presents ConvRerank, a conversational passage re-ranker that employs a newly developed pseudo-labeling approach. Our proposed view-ensemble method enhances the quality of pseudo-labeled data, thus improving th…
Conversational SearchPassage Re-RankingRe-RankingT2Ranking: A large-scale Chinese Benchmark for Passage Ranking
Passage ranking involves two stages: passage retrieval and passage re-ranking, which are important and challenging topics for both academics and industries in the area of Information Retrieval (IR). However, the commonly…
Information RetrievalPassage RankingPassage Re-RankingPassage Retrieval+2Few-shot Reranking for Multi-hop QA via Language Model Prompting
We study few-shot reranking for multi-hop QA with open-domain questions. To alleviate the need for a large number of labeled question-document pairs for retriever training, we propose PromptRank, which relies on large la…
Language ModelingOpen-Domain Question AnsweringPassage Re-RankingReranking+2HLATR: Enhance Multi-stage Text Retrieval with Hybrid List Aware Transformer Reranking
Deep pre-trained language models (e,g. BERT) are effective at large-scale text retrieval task. Existing text retrieval systems with state-of-the-art performance usually adopt a retrieve-then-reranking architecture due to…
Passage RankingPassage Re-RankingRerankingRetrieval+1Incorporating Explicit Knowledge in Pre-trained Language Models for Passage Re-ranking
Passage re-ranking is to obtain a permutation over the candidate passage set from retrieval stage. Re-rankers have been boomed by Pre-trained Language Models (PLMs) due to their overwhelming advantages in natural languag…
Graph Neural NetworkNatural Language UnderstandingPassage Re-RankingRe-Ranking+1Quality and Cost Trade-offs in Passage Re-ranking Task
Deep learning models named transformers achieved state-of-the-art results in a vast majority of NLP tasks at the cost of increased computational complexity and high memory consumption. Using the transformer model in real…
Information RetrievalOpen-Domain Question AnsweringPassage Re-RankingRe-Ranking+1Towards Robust Passage Re-Ranking Model by Mitigating Lexical Match Bias
While deep learning models can overcome the limitations of traditional machine learning algorithms that use hand-crafted features, recent studies have shown that these models often achieve high dataset-specific accuracy …
Information RetrievalPassage Re-RankingRe-RankingRetrievalRocketQAv2: A Joint Training Method for Dense Passage Retrieval and Passage Re-ranking
In various natural language processing tasks, passage retrieval and passage re-ranking are two key procedures in finding and ranking relevant information. Since both the two procedures contribute to the final performance…
Data AugmentationInformation RetrievalNatural QuestionsOpen-Domain Question Answering+4Dealing with Typos for BERT-based Passage Retrieval and Ranking
Passage retrieval and ranking is a key task in open-domain question answering and information retrieval. Current effective approaches mostly rely on pre-trained deep language model-based retrievers and rankers. These met…
Information RetrievalLanguage ModelingLanguage ModellingOpen-Domain Question Answering+6Fast Passage Re-ranking with Contextualized Exact Term Matching and Efficient Passage Expansion
BERT-based information retrieval models are expensive, in both time (query latency) and computational resources (energy, hardware cost), making many of these models impractical especially under resource constraints. The …
CPUInformation RetrievalPassage Re-RankingRe-Ranking+1A Modern Perspective on Query Likelihood with Deep Generative Retrieval Models
Existing neural ranking models follow the text matching paradigm, where document-to-query relevance is estimated through predicting the matching score. Drawing from the rich literature of classical generative retrieval m…
Passage Re-RankingPassage RetrievalRe-RankingRetrieval+1