Papers Re-Ranking
“Re-Ranking” 태그가 달린 논문 743편 · 필터 해제
Beyond Utility: Evaluating LLM as Recommender
With the rapid development of Large Language Models (LLMs), recent studies employed LLMs as recommenders to provide personalized information services for distinct users. Despite efforts to improve the accuracy of LLM-bas…
PositionRe-RankingEfficient and Interpretable Grammatical Error Correction with Mixture of Experts
Error type information has been widely used to improve the performance of grammatical error correction (GEC) models, whether for generating corrections, re-ranking them, or combining GEC models. Combining GEC models that…
Grammatical Error CorrectionMixture-of-ExpertsRe-RankingBeyond Positive History: Re-ranking with List-level Hybrid Feedback
As the last stage of recommender systems, re-ranking generates a re-ordered list that aligns with the user's preference. However, previous works generally focus on item-level positive feedback as history (e.g., only clic…
Contrastive LearningRecommendation SystemsRe-RankingUnsupervised Feature Selection Algorithm Based on Dual Manifold Re-ranking
High-dimensional data is commonly encountered in numerous data analysis tasks. Feature selection techniques aim to identify the most representative features from the original high-dimensional data. Due to the absence of …
feature selectionRe-RankingQuam: Adaptive Retrieval through Query Affinity Modelling
Building relevance models to rank documents based on user information needs is a central task in information retrieval and the NLP community. Beyond the direct ad-hoc search setting, many knowledge-intense tasks are powe…
Information RetrievalRe-RankingRetrievalOn Model-Free Re-ranking for Visual Place Recognition with Deep Learned Local Features
Re-ranking is the second stage of a visual place recognition task, in which the system chooses the best-matching images from a pre-selected subset of candidates. Model-free approaches compute the image pair similarity ba…
Re-RankingVisual Place RecognitionBridging Search and Recommendation in Generative Retrieval: Does One Task Help the Other?
Generative retrieval for search and recommendation is a promising paradigm for retrieving items, offering an alternative to traditional methods that depend on external indexes and nearest-neighbor searches. Instead, gene…
Collaborative FilteringInformation RetrievalRe-RankingResponse Generation+1Enhancing Retrieval Performance: An Ensemble Approach For Hard Negative Mining
Ranking consistently emerges as a primary focus in information retrieval research. Retrieval and ranking models serve as the foundation for numerous applications, including web search, open domain QA, enterprise domain Q…
Information RetrievalRAGRecommendation SystemsRe-Ranking+2Steering Your Generalists: Improving Robotic Foundation Models via Value Guidance
Large, general-purpose robotic policies trained on diverse demonstration datasets have been shown to be remarkably effective both for controlling a variety of robots in a range of different scenes, and for acquiring broa…
Offline RLRe-RankingMAIR: A Massive Benchmark for Evaluating Instructed Retrieval
Recent information retrieval (IR) models are pre-trained and instruction-tuned on massive datasets and tasks, enabling them to perform well on a wide range of tasks and potentially generalize to unseen tasks with instruc…
Information RetrievalRe-RankingRetrievalInstructG2I: Synthesizing Images from Multimodal Attributed Graphs
In this paper, we approach an overlooked yet critical task Graph2Image: generating images from multimodal attributed graphs (MMAGs). This task poses significant challenges due to the explosion in graph size, dependencies…
DenoisingRe-RankingPair-VPR: Place-Aware Pre-training and Contrastive Pair Classification for Visual Place Recognition with Vision Transformers
In this work we propose a novel joint training method for Visual Place Recognition (VPR), which simultaneously learns a global descriptor and a pair classifier for re-ranking. The pair classifier can predict whether a gi…
DecoderRe-RankingVisual Place RecognitionContext-Augmented Code Generation Using Programming Knowledge Graphs
Large Language Models (LLMs) and Code-LLMs (CLLMs) have significantly improved code generation, but, they frequently face difficulties when dealing with challenging and complex problems. Retrieval-Augmented Generation (R…
Code GenerationHumanEvalKnowledge Graphsmbpp+4Direct Preference Optimization for LLM-Enhanced Recommendation Systems
Large Language Models (LLMs) have exhibited remarkable performance across a wide range of domains, motivating research into their potential for recommendation systems. Early efforts have leveraged LLMs' rich knowledge an…
In-Context LearningInstruction FollowingRecommendation Systemsreinforcement-learning+3RRADistill: Distilling LLMs' Passage Ranking Ability for Long-Tail Queries Document Re-Ranking on a Search Engine
Large Language Models (LLMs) excel at understanding the semantic relationships between queries and documents, even with lengthy and complex long-tail queries. These queries are challenging for feedback-based rankings due…
DecoderPassage RankingRe-RankingBeyond Correlation: Interpretable Evaluation of Machine Translation Metrics
Machine Translation (MT) evaluation metrics assess translation quality automatically. Recently, researchers have employed MT metrics for various new use cases, such as data filtering and translation re-ranking. However, …
Machine TranslationRe-RankingTranslationAttention in Large Language Models Yields Efficient Zero-Shot Re-Rankers
Information retrieval (IR) systems have played a vital role in modern digital life and have cemented their continued usefulness in this new era of generative AI via retrieval-augmented generation. With strong language pr…
Information RetrievalRe-RankingRetrievalRetrieval-augmented Generation+1Inductive Generative Recommendation via Retrieval-based Speculation
Generative recommendation (GR) is an emerging paradigm that tokenizes items into discrete tokens and learns to autoregressively generate the next tokens as predictions. Although effective, GR models operate in a transduc…
Re-RankingRetrievalMulti-Target Cross-Lingual Summarization: a novel task and a language-neutral approach
Cross-lingual summarization aims to bridge language barriers by summarizing documents in different languages. However, ensuring semantic coherence across languages is an overlooked challenge and can be critical in severa…
Re-RankingImproving Multilingual ASR in the Wild Using Simple N-best Re-ranking
Multilingual Automatic Speech Recognition (ASR) models are typically evaluated in a setting where the ground-truth language of the speech utterance is known, however, this is often not the case for most practical setting…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Language IdentificationRe-Ranking+3