Papers Re-Ranking
“Re-Ranking” 태그가 달린 논문 743편 · 필터 해제
Balancing Lexical and Semantic Quality in Abstractive Summarization
An important problem of the sequence-to-sequence neural models widely used in abstractive summarization is exposure bias. To alleviate this problem, re-ranking systems have been applied in recent years. Despite some perf…
Abstractive Text SummarizationRe-RankingSemantic SimilaritySemantic Textual SimilarityEvaluating Embedding APIs for Information Retrieval
The ever-increasing size of language models curtails their widespread availability to the community, thereby galvanizing many companies into offering access to large language models through APIs. One particular type, sui…
Domain GeneralizationInformation RetrievalRe-RankingRetrievalEmpowering Language Model with Guided Knowledge Fusion for Biomedical Document Re-ranking
Pre-trained language models (PLMs) have proven to be effective for document re-ranking task. However, they lack the ability to fully interpret the semantics of biomedical and health-care queries and often rely on simplis…
Language ModelingLanguage ModellingRe-RankingFairness in Image Search: A Study of Occupational Stereotyping in Image Retrieval and its Debiasing
Multi-modal search engines have experienced significant growth and widespread use in recent years, making them the second most common internet use. While search engine systems offer a range of services, the image search …
FairnessImage RetrievalInformation RetrievalRe-Ranking+1Pay More Attention to Relation Exploration for Knowledge Base Question Answering
Knowledge base question answering (KBQA) is a challenging task that aims to retrieve correct answers from large-scale knowledge bases. Existing attempts primarily focus on entity representation and final answer reasoning…
Knowledge Base Question AnsweringQuestion AnsweringRelationRe-RankingGenerating Synthetic Documents for Cross-Encoder Re-Rankers: A Comparative Study of ChatGPT and Human Experts
We investigate the usefulness of generative Large Language Models (LLMs) in generating training data for cross-encoder re-rankers in a novel direction: generating synthetic documents instead of synthetic queries. We intr…
Re-RankingRetrievalLearning to Re-rank with Constrained Meta-Optimal Transport
Many re-ranking strategies in search systems rely on stochastic ranking policies, encoded as Doubly-Stochastic (DS) matrices, that satisfy desired ranking constraints in expectation, e.g., Fairness of Exposure (FOE). The…
FairnessLearning-To-RankRe-RankingPerson Re-ID through Unsupervised Hypergraph Rank Selection and Fusion
Person Re-ID has been gaining a lot of attention and nowadays is of fundamental importance in many camera surveillance applications. The task consists of identifying individuals across multiple cameras that have no overl…
Re-RankingSTIR: Siamese Transformer for Image Retrieval Postprocessing
Current metric learning approaches for image retrieval are usually based on learning a space of informative latent representations where simple approaches such as the cosine distance will work well. Recent state of the a…
Image RetrievalMetric LearningRepresentation LearningReranking+3Improving 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-RankingExploiting Simulated User Feedback for Conversational Search: Ranking, Rewriting, and Beyond
This research aims to explore various methods for assessing user feedback in mixed-initiative conversational search (CS) systems. While CS systems enjoy profuse advancements across multiple aspects, recent research fails…
Conversational SearchPassage RetrievalRe-RankingRetrievalLearnable Pillar-based Re-ranking for Image-Text Retrieval
Image-text retrieval aims to bridge the modality gap and retrieve cross-modal content based on semantic similarities. Prior work usually focuses on the pairwise relations (i.e., whether a data sample matches another) but…
Image-text RetrievalRe-RankingRetrievalText RetrievalIs ChatGPT Good at Search? Investigating Large Language Models as Re-Ranking Agents
Large Language Models (LLMs) have demonstrated remarkable zero-shot generalization across various language-related tasks, including search engines. However, existing work utilizes the generative ability of LLMs for Infor…
Information RetrievalPassage RankingRe-RankingRetrieval+1FairRec: Fairness Testing for Deep Recommender Systems
Deep learning-based recommender systems (DRSs) are increasingly and widely deployed in the industry, which brings significant convenience to people's daily life in different ways. However, recommender systems are also sh…
FairnessRecommendation SystemsRe-RankingAre Local Features All You Need for Cross-Domain Visual Place Recognition?
Visual Place Recognition is a task that aims to predict the coordinates of an image (called query) based solely on visual clues. Most commonly, a retrieval approach is adopted, where the query is matched to the most simi…
AllRe-RankingRetrievalVisual Place RecognitionT2Ranking: 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+2Black Box Few-Shot Adaptation for Vision-Language models
Vision-Language (V-L) models trained with contrastive learning to align the visual and language modalities have been shown to be strong few-shot learners. Soft prompt learning is the method of choice for few-shot downstr…
Contrastive LearningPrompt LearningRe-RankingDialogue-Contextualized Re-ranking for Medical History-Taking
AI-driven medical history-taking is an important component in symptom checking, automated patient intake, triage, and other AI virtual care applications. As history-taking is extremely varied, machine learning models req…
Language ModelingLanguage ModellingRe-RankingIf At First You Don't Succeed: Test Time Re-ranking for Zero-shot, Cross-domain Retrieval
In this paper we propose a novel method for zero-shot, cross-domain image retrieval in which we make two key contributions. The first is a test-time re-ranking procedure that enables query-gallery pairs, without meaningf…
Image RetrievalKnowledge DistillationRe-RankingRetrieval+1Data-efficient Large Scale Place Recognition with Graded Similarity Supervision
Visual place recognition (VPR) is a fundamental task of computer vision for visual localization. Existing methods are trained using image pairs that either depict the same place or not. Such a binary indication does not …
Re-RankingVisual LocalizationVisual Place Recognition