Papers Re-Ranking
“Re-Ranking” 태그가 달린 논문 743편 · 필터 해제
A Flexible and Scalable Framework for Video Moment Search
Video moment search, the process of finding relevant moments in a video corpus to match a user's query, is crucial for various applications. Existing solutions, however, often assume a single perfect matching moment, str…
Moment RetrievalRe-RankingRetrievalRe-ranking the Context for Multimodal Retrieval Augmented Generation
Retrieval-augmented generation (RAG) enhances large language models (LLMs) by incorporating external knowledge to generate a response within a context with improved accuracy and reduced hallucinations. However, multi-mod…
RAGRe-RankingRetrievalRetrieval-augmented GenerationMultilingual Open QA on the MIA Shared Task
Cross-lingual information retrieval (CLIR) ~\cite{shi2021cross, asai2021one, jiang2020cross} for example, can find relevant text in any language such as English(high resource) or Telugu (low resource) even when the query…
Cross-Lingual Information RetrievalInformation RetrievalPassage RetrievalQuestion Answering+4Cheb-GR: Rethinking K-nearest Neighbor Search in Re-ranking for Person Re-identification
Person re-identification (ReID) is the task of matching individuals across different camera views. Existing approaches typically employ neural networks to extract discriminative features, ranking gallery images based…
Person Re-IdentificationRe-RankingRetrievalPre-training, Fine-tuning and Re-ranking: A Three-Stage Framework for Legal Question Answering
Legal question answering (QA) has attracted increasing attention from people seeking legal advice, which aims to retrieve the most applicable answers from a large-scale database of question-answer pairs. Previous methods…
Question AnsweringRepresentation LearningRe-RankingImproving Generated and Retrieved Knowledge Combination Through Zero-shot Generation
Open-domain Question Answering (QA) has garnered substantial interest by combining the advantages of faithfully retrieved passages and relevant passages generated through Large Language Models (LLMs). However, there is a…
Open-Domain Question AnsweringQuestion AnsweringRerankingRe-Ranking+1EMPRA: Embedding Perturbation Rank Attack against Neural Ranking Models
Recent research has shown that neural information retrieval techniques may be susceptible to adversarial attacks. Adversarial attacks seek to manipulate the ranking of documents, with the intention of exposing users to t…
Adversarial TextInformation RetrievalRe-RankingSentence+1Denoising Nearest Neighbor Graph via Continuous CRF for Visual Re-ranking without Fine-tuning
Visual re-ranking using Nearest Neighbor graph~(NN graph) has been adapted to yield high retrieval accuracy, since it is beneficial to exploring an high-dimensional manifold and applicable without additional fine-tuning.…
DenoisingPerson Re-IdentificationRe-RankingRetrievalLitLLMs, LLMs for Literature Review: Are we there yet?
Literature reviews are an essential component of scientific research, but they remain time-intensive and challenging to write, especially due to the recent influx of research papers. This paper explores the zero-shot abi…
Re-RankingRetrievalRAG-based Question Answering over Heterogeneous Data and Text
This article presents the QUASAR system for question answering over unstructured text, structured tables, and knowledge graphs, with unified treatment of all sources. The system adopts a RAG-based architecture, with a pi…
Answer GenerationKnowledge GraphsLanguage ModelingLanguage Modelling+4Policy Agnostic RL: Offline RL and Online RL Fine-Tuning of Any Class and Backbone
Recent advances in learning decision-making policies can largely be attributed to training expressive policy models, largely via imitation learning. While imitation learning discards non-expert data, reinforcement learni…
global-optimizationImitation LearningOffline RLReinforcement Learning (RL)+1Semantic Retrieval at Walmart
In product search, the retrieval of candidate products before re-ranking is more critical and challenging than other search like web search, especially for tail queries, which have a complex and specific search intent. I…
Re-RankingRetrievalSemantic RetrievalTime-Reversal Provides Unsupervised Feedback to LLMs
Large Language Models (LLMs) are typically trained to predict in the forward direction of time. However, recent works have shown that prompting these models to look back and critique their own generations can produce use…
Passage RetrievalRe-RankingEDTformer: An Efficient Decoder Transformer for Visual Place Recognition
Visual place recognition (VPR) aims to determine the general geographical location of a query image by retrieving visually similar images from a large geo-tagged database. To obtain a global representation for each place…
DecoderRe-RankingVisual Place RecognitionFairSort: Learning to Fair Rank for Personalized Recommendations in Two-Sided Platforms
Traditional recommendation systems focus on maximizing user satisfaction by suggesting their favourite items. This user-centric approach may lead to unfair exposure distribution among the providers. On the contrary, a pr…
FairnessRecommendation SystemsRe-RankingTask Progressive Curriculum Learning for Robust Visual Question Answering
Visual Question Answering (VQA) systems are known for their poor performance in out-of-distribution datasets. An issue that was addressed in previous works through ensemble learning, answer re-ranking, or artificially gr…
Data AugmentationEnsemble LearningQuestion AnsweringRe-Ranking+2SymphonyQG: Towards Symphonious Integration of Quantization and Graph for Approximate Nearest Neighbor Search
Approximate nearest neighbor (ANN) search in high-dimensional Euclidean space has a broad range of applications. Among existing ANN algorithms, graph-based methods have shown superior performance in terms of the time-acc…
QuantizationRe-RankingAdvancing Large Language Models for Spatiotemporal and Semantic Association Mining of Similar Environmental Events
Retrieval and recommendation are two essential tasks in modern search tools. This paper introduces a novel retrieval-reranking framework leveraging Large Language Models (LLMs) to enhance the spatiotemporal and semantic …
ArticlesRerankingRe-RankingRetrieval+2Retrieval, Reasoning, Re-ranking: A Context-Enriched Framework for Knowledge Graph Completion
The Knowledge Graph Completion~(KGC) task aims to infer the missing entity from an incomplete triple. Existing embedding-based methods rely solely on triples in the KG, which is vulnerable to specious relation patterns a…
Knowledge Graph CompletionLanguage ModelingLanguage ModellingLarge Language Model+2Lightning IR: Straightforward Fine-tuning and Inference of Transformer-based Language Models for Information Retrieval
A wide range of transformer-based language models have been proposed for information retrieval tasks. However, including transformer-based models in retrieval pipelines is often complex and requires substantial engineeri…
Information RetrievalRe-RankingRetrieval