Papers Re-Ranking
“Re-Ranking” 태그가 달린 논문 743편 · 필터 해제
EcoRank: Budget-Constrained Text Re-ranking Using Large Language Models
Large Language Models (LLMs) have achieved state-of-the-art performance in text re-ranking. This process includes queries and candidate passages in the prompts, utilizing pointwise, listwise, and pairwise prompting strat…
Passage RerankingRerankingRe-RankingA Systematic Review of Data-to-Text NLG
This systematic review undertakes a comprehensive analysis of current research on data-to-text generation, identifying gaps, challenges, and future directions within the field. Relevant literature in this field on datase…
Data-to-Text GenerationHallucinationRe-RankingText GenerationBryndza at ClimateActivism 2024: Stance, Target and Hate Event Detection via Retrieval-Augmented GPT-4 and LLaMA
This study details our approach for the CASE 2024 Shared Task on Climate Activism Stance and Hate Event Detection, focusing on Hate Speech Detection, Hate Speech Target Identification, and Stance Detection as classificat…
Event DetectionHate Speech DetectionRe-RankingRetrieval+1Constrained Multiview Representation for Self-supervised Contrastive Learning
Representation learning constitutes a pivotal cornerstone in contemporary deep learning paradigms, offering a conduit to elucidate distinctive features within the latent space and interpret the deep models. Nevertheless,…
Contrastive LearningDisentanglementImage SegmentationMedical Image Segmentation+3A Personalized Framework for Consumer and Producer Group Fairness Optimization in Recommender Systems
In recent years, there has been an increasing recognition that when machine learning (ML) algorithms are used to automate decisions, they may mistreat individuals or groups, with legal, ethical, or economic implications.…
Collaborative FilteringFairnessModel SelectionRecommendation Systems+1Regressing Transformers for Data-efficient Visual Place Recognition
Visual place recognition is a critical task in computer vision, especially for localization and navigation systems. Existing methods often rely on contrastive learning: image descriptors are trained to have small distanc…
Contrastive LearningRe-RankingVisual Place RecognitionT-Rex: Text-assisted Retrosynthesis Prediction
As a fundamental task in computational chemistry, retrosynthesis prediction aims to identify a set of reactants to synthesize a target molecule. Existing template-free approaches only consider the graph structures of the…
Computational chemistryPredictionRe-RankingRetrosynthesisQuery Exposure Prediction for Groups of Documents in Rankings
The main objective of an Information Retrieval system is to provide a user with the most relevant documents to the user's query. To do this, modern IR systems typically deploy a re-ranking pipeline in which a set of docu…
Information RetrievalPredictionRe-RankingRetrievalEnhancing Recommendation Diversity by Re-ranking with Large Language Models
It has long been recognized that it is not enough for a Recommender System (RS) to provide recommendations based only on their relevance to users. Among many other criteria, the set of recommendations may need to be dive…
DiversityRecommendation SystemsRe-RankingReFT: Reasoning with Reinforced Fine-Tuning
One way to enhance the reasoning capability of Large Language Models (LLMs) is to conduct Supervised Fine-Tuning (SFT) using Chain-of-Thought (CoT) annotations. This approach does not show sufficiently strong generalizat…
GSM8KMathRe-RankingRevealing the Hidden Impact of Top-N Metrics on Optimization in Recommender Systems
The hyperparameters of recommender systems for top-n predictions are typically optimized to enhance the predictive performance of algorithms. Thereby, the optimization algorithm, e.g., grid search or random search, searc…
Recommendation SystemsRe-RankingDQNC2S: DQN-based Cross-stream Crisis event Summarizer
Summarizing multiple disaster-relevant data streams simultaneously is particularly challenging as existing Retrieve&Re-ranking strategies suffer from the inherent redundancy of multi-stream data and limited scalability i…
Re-RankingExploring the Best Practices of Query Expansion with Large Language Models
Large Language Models (LLMs) are foundational in language technologies, particularly in information retrieval (IR). Previous studies have utilized LLMs for query expansion, achieving notable improvements in IR. In this p…
Information RetrievalRe-RankingRetrievalText Generation+1DREQ: Document Re-Ranking Using Entity-based Query Understanding
While entity-oriented neural IR models have advanced significantly, they often overlook a key nuance: the varying degrees of influence individual entities within a document have on its overall relevance. Addressing this …
Re-RankingMultiSlot ReRanker: A Generic Model-based Re-Ranking Framework in Recommendation Systems
In this paper, we propose a generic model-based re-ranking framework, MultiSlot ReRanker, which simultaneously optimizes relevance, diversity, and freshness. Specifically, our Sequential Greedy Algorithm (SGA) is efficie…
DiversityOpenAI GymRecommendation SystemsRe-RankingConcurrent Brainstorming & Hypothesis Satisfying: An Iterative Framework for Enhanced Retrieval-Augmented Generation (R2CBR3H-SR)
Addressing the complexity of comprehensive information retrieval, this study introduces an innovative, iterative retrieval-augmented generation system. Our approach uniquely integrates a vector-space driven re-ranking me…
Decision MakingInformation RetrievalRe-RankingRetrieval+1Investigating the Effects of Sparse Attention on Cross-Encoders
Cross-encoders are effective passage and document re-rankers but less efficient than other neural or classic retrieval models. A few previous studies have applied windowed self-attention to make cross-encoders more effic…
Re-RankingRetrievalGUITAR: Gradient Pruning toward Fast Neural Ranking
With the continuous popularity of deep learning and representation learning, fast vector search becomes a vital task in various ranking/retrieval based applications, say recommendation, ads ranking and question answering…
Question AnsweringRepresentation LearningRe-RankingTraining-free Zero-shot Composed Image Retrieval with Local Concept Reranking
Composed image retrieval attempts to retrieve an image of interest from gallery images through a composed query of a reference image and its corresponding modified text. It has recently attracted attention due to the col…
Image RetrievalRerankingRe-RankingRetrieval+2Contextually Affinitive Neighborhood Refinery for Deep Clustering
Previous endeavors in self-supervised learning have enlightened the research of deep clustering from an instance discrimination perspective. Built upon this foundation, recent studies further highlight the importance of …
ClusteringDeep ClusteringImage RetrievalRe-Ranking+2