Papers Re-Ranking
“Re-Ranking” 태그가 달린 논문 743편 · 필터 해제
Relevance Feedback with Brain Signals
The Relevance Feedback (RF) process relies on accurate and real-time relevance estimation of feedback documents to improve retrieval performance. Since collecting explicit relevance annotations imposes an extra burden on…
Brain Computer InterfaceRe-RankingESPN: Memory-Efficient Multi-Vector Information Retrieval
Recent advances in large language models have demonstrated remarkable effectiveness in information retrieval (IR) tasks. While many neural IR systems encode queries and documents into single-vector representations, multi…
Information RetrievalRe-RankingRetrievalOptimal Transport Aggregation for Visual Place Recognition
The task of Visual Place Recognition (VPR) aims to match a query image against references from an extensive database of images from different places, relying solely on visual cues. State-of-the-art pipelines focus on the…
Re-RankingVisual Place RecognitionVideo-based Visible-Infrared Person Re-Identification with Auxiliary Samples
Visible-infrared person re-identification (VI-ReID) aims to match persons captured by visible and infrared cameras, allowing person retrieval and tracking in 24-hour surveillance systems. Previous methods focus on learni…
Generative Adversarial NetworkPerson Re-IdentificationPerson RetrievalRe-RankingRelevance feedback strategies for recall-oriented neural information retrieval
In a number of information retrieval applications (e.g., patent search, literature review, due diligence, etc.), preventing false negatives is more important than preventing false positives. However, approaches designed …
Active LearningInformation RetrievalRe-RankingRetrievalCA-Jaccard: Camera-aware Jaccard Distance for Person Re-identification
Person re-identification (re-ID) is a challenging task that aims to learn discriminative features for person retrieval. In person re-ID, Jaccard distance is a widely used distance metric, especially in re-ranking and clu…
Person Re-IdentificationPerson RetrievalRe-RankingUnsupervised Person Re-Identification+2RED-DOT: Multimodal Fact-checking via Relevant Evidence Detection
Online misinformation is often multimodal in nature, i.e., it is caused by misleading associations between texts and accompanying images. To support the fact-checking process, researchers have been recently developing au…
Fact CheckingMisinformationRe-RankingText Retrieval with Multi-Stage Re-Ranking Models
The text retrieval is the task of retrieving similar documents to a search query, and it is important to improve retrieval accuracy while maintaining a certain level of retrieval speed. Existing studies have reported acc…
Language ModelingLanguage ModellingRe-RankingRetrieval+1Citation Recommendation on Scholarly Legal Articles
Citation recommendation is the task of finding appropriate citations based on a given piece of text. The proposed datasets for this task consist mainly of several scientific fields, lacking some core ones, such as law. F…
ArticlesCitation RecommendationRe-RankingDialogizer: Context-aware Conversational-QA Dataset Generation from Textual Sources
To address the data scarcity issue in Conversational question answering (ConvQA), a dialog inpainting method, which utilizes documents to generate ConvQA datasets, has been proposed. However, the original dialog inpainti…
Conversational Question AnsweringDataset GenerationQuestion AnsweringRe-RankingFast and Interpretable Face Identification for Out-Of-Distribution Data Using Vision Transformers
Most face identification approaches employ a Siamese neural network to compare two images at the image embedding level. Yet, this technique can be subject to occlusion (e.g. faces with masks or sunglasses) and out-of-dis…
Face IdentificationRe-RankingEfficient Neural Ranking using Forward Indexes and Lightweight Encoders
Dual-encoder-based dense retrieval models have become the standard in IR. They employ large Transformer-based language models, which are notoriously inefficient in terms of resources and latency. We propose Fast-Forward …
Computational EfficiencyRe-RankingRetrievalGAR-meets-RAG Paradigm for Zero-Shot Information Retrieval
Given a query and a document corpus, the information retrieval (IR) task is to output a ranked list of relevant documents. Combining large language models (LLMs) with embedding-based retrieval models, recent work shows p…
Information RetrievalPassage RetrievalRAGRe-Ranking+2Jina Embeddings 2: 8192-Token General-Purpose Text Embeddings for Long Documents
Text embedding models have emerged as powerful tools for transforming sentences into fixed-sized feature vectors that encapsulate semantic information. While these models are essential for tasks like information retrieva…
Information RetrievalMTEB BenchmarkRe-RankingRetrievalProMap: Effective Bilingual Lexicon Induction via Language Model Prompting
Bilingual Lexicon Induction (BLI), where words are translated between two languages, is an important NLP task. While noticeable progress on BLI in rich resource languages using static word embeddings has been achieved. T…
Bilingual Lexicon InductionLanguage ModelingLanguage ModellingRe-Ranking+3PaRaDe: 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+4Contextual Data Augmentation for Task-Oriented Dialog Systems
Collection of annotated dialogs for training task-oriented dialog systems have been one of the key bottlenecks in improving current models. While dialog response generation has been widely studied on the agent side, it i…
Data AugmentationLanguage ModelingLanguage ModellingParaphrase Generation+2Improving Large Language Model Fine-tuning for Solving Math Problems
Despite their success in many natural language tasks, solving math problems remains a significant challenge for large language models (LLMs). A large gap exists between LLMs' pass-at-one and pass-at-N performance in solv…
Language ModelingLanguage ModellingLarge Language ModelMath+1Ranking LLM-Generated Loop Invariants for Program Verification
Synthesizing inductive loop invariants is fundamental to automating program verification. In this work, we observe that Large Language Models (such as gpt-3.5 or gpt-4) are capable of synthesizing loop invariants for a c…
Re-RankingRethinking Large-scale Pre-ranking System: Entire-chain Cross-domain Models
Industrial systems such as recommender systems and online advertising, have been widely equipped with multi-stage architectures, which are divided into several cascaded modules, including matching, pre-ranking, ranking a…
Recommendation SystemsRe-RankingSelection bias