Papers Reranking
“Reranking” 태그가 달린 논문 586편 · 필터 해제
SARA: Selective and Adaptive Retrieval-augmented Generation with Context Compression
Retrieval-augmented Generation (RAG) extends large language models (LLMs) with external knowledge but faces key challenges: restricted effective context length and redundancy in retrieved documents. Pure compression-base…
Evidence SelectionRAGRerankingRetrieval+4Efficiency-Effectiveness Reranking FLOPs for LLM-based Rerankers
Large Language Models (LLMs) have recently been applied to reranking tasks in information retrieval, achieving strong performance. However, their high computational demands often hinder practical deployment. Existing stu…
Information RetrievalRerankingMask-aware Text-to-Image Retrieval: Referring Expression Segmentation Meets Cross-modal Retrieval
Text-to-image retrieval (TIR) aims to find relevant images based on a textual query, but existing approaches are primarily based on whole-image captions and lack interpretability. Meanwhile, referring expression segmenta…
Cross-Modal RetrievalImage CaptioningImage RetrievalLarge Language Model+9JointRank: Rank Large Set with Single Pass
Efficiently ranking relevant items from large candidate pools is a cornerstone of modern information retrieval systems -- such as web search, recommendation, and retrieval-augmented generation. Listwise rerankers, which …
Information RetrievalRerankingRetrievalRetrieval-augmented GenerationLeveraging LLM-Assisted Query Understanding for Live Retrieval-Augmented Generation
Real-world live retrieval-augmented generation (RAG) systems face significant challenges when processing user queries that are often noisy, ambiguous, and contain multiple intents. While RAG enhances large language model…
RAGRerankingRetrievalRetrieval-augmented GenerationAI Assistants to Enhance and Exploit the PETSc Knowledge Base
Generative AI, especially through large language models (LLMs), is transforming how technical knowledge can be accessed, reused, and extended. PETSc, a widely used numerical library for high-performance scientific comput…
RAGRerankingRetrieval-augmented Generationscientific discoverySACL: Understanding and Combating Textual Bias in Code Retrieval with Semantic-Augmented Reranking and Localization
Retrieval-Augmented Code Generation (RACG) is a critical technique for enhancing code generation by retrieving relevant information. In this work, we conduct an in-depth analysis of code retrieval by systematically maski…
Code GenerationHumanEvalmbppReranking+1Knowledge-Aware Diverse Reranking for Cross-Source Question Answering
This paper presents Team Marikarp's solution for the SIGIR 2025 LiveRAG competition. The competition's evaluation set, automatically generated by DataMorgana from internet corpora, encompassed a wide range of target topi…
Question AnsweringRAGRerankingInsertRank: LLMs can reason over BM25 scores to Improve Listwise Reranking
Large Language Models (LLMs) have demonstrated significant strides across various information retrieval tasks, particularly as rerankers, owing to their strong generalization and knowledge-transfer capabilities acquired …
Information RetrievalRerankingRetrievalSemantic Similarity+2MM-R5: MultiModal Reasoning-Enhanced ReRanker via Reinforcement Learning for Document Retrieval
Multimodal document retrieval systems enable information access across text, images, and layouts, benefiting various domains like document-based question answering, report analysis, and interactive content summarization.…
Instruction FollowingMultimodal ReasoningQuestion AnsweringReinforcement Learning (RL)+2Constructing and Evaluating Declarative RAG Pipelines in PyTerrier
Search engines often follow a pipeline architecture, where complex but effective reranking components are used to refine the results of an initial retrieval. Retrieval augmented generation (RAG) is an exciting applicatio…
Natural QuestionsRAGRerankingRetrieval+1MEMETRON: Metaheuristic Mechanisms for Test-time Response Optimization of Large Language Models
Large language models (LLMs) are increasingly used for both open-ended and structured tasks, yet their inference-time behavior is still largely dictated by heuristic decoding strategies such as greedy search, sampling, o…
RerankingKnowledge Compression via Question Generation: Enhancing Multihop Document Retrieval without Fine-tuning
This study presents a question-based knowledge encoding approach that improves retrieval-augmented generation (RAG) systems without requiring fine-tuning or traditional chunking. We encode textual content using generated…
ChunkingQuestion GenerationQuestion-GenerationRAG+3Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models
In this work, we introduce the Qwen3 Embedding series, a significant advancement over its predecessor, the GTE-Qwen series, in text embedding and reranking capabilities, built upon the Qwen3 foundation models. Leveraging…
RerankingRetrievalUnsupervised Pre-trainingREARANK: Reasoning Re-ranking Agent via Reinforcement Learning
We present REARANK, a large language model (LLM)-based listwise reasoning reranking agent. REARANK explicitly reasons before reranking, significantly improving both performance and interpretability. Leveraging reinforcem…
Data AugmentationInformation RetrievalLanguage ModelingLanguage Modelling+6RankLLM: A Python Package for Reranking with LLMs
The adoption of large language models (LLMs) as rerankers in multi-stage retrieval systems has gained significant traction in academia and industry. These models refine a candidate list of retrieved documents, often thro…
Mixture-of-ExpertsRAGRerankingRetrieval+1AcuRank: Uncertainty-Aware Adaptive Computation for Listwise Reranking
Listwise reranking with large language models (LLMs) enhances top-ranked results in retrieval-based applications. Due to the limit in context size and high inference cost of long context, reranking is typically performed…
RerankingRetrievalUniversal Biological Sequence Reranking for Improved De Novo Peptide Sequencing
De novo peptide sequencing is a critical task in proteomics. However, the performance of current deep learning-based methods is limited by the inherent complexity of mass spectrometry data and the heterogeneous distribut…
de novo peptide sequencingRerankingZero-shot GeneralizationDon't "Overthink" Passage Reranking: Is Reasoning Truly Necessary?
With the growing success of reasoning models across complex natural language tasks, researchers in the Information Retrieval (IR) community have begun exploring how similar reasoning capabilities can be integrated into p…
Information RetrievalPassage RerankingRerankingReranking with Compressed Document Representation
Reranking, the process of refining the output of a first-stage retriever, is often considered computationally expensive, especially with Large Language Models. Borrowing from recent advances in document compression for R…
RAGReranking