AGRaME: Any-Granularity Ranking with Multi-Vector Embeddings
Ranking is a fundamental and popular problem in search. However, existing ranking algorithms usually restrict the granularity of ranking to full passages or require a specific dense index for each desired level of granularity. Such lack of flexibility in granularity negatively affects many applications that can benefit from more granular ranking, such as sentence-level ranking for open-domain question-answering, or proposition-level ranking for attribution. In this work, we introduce the idea of any-granularity ranking, which leverages multi-vector embeddings to rank at varying levels of granularity while maintaining encoding at a single (coarser) level of granularity. We propose a multi-granular contrastive loss for training multi-vector approaches, and validate its utility with both sentences and propositions as ranking units. Finally, we demonstrate the application of proposition-level ranking to post-hoc citation addition in retrieval-augmented generation, surpassing the performance of prompt-driven citation generation.
Code (0)
등록된 구현이 없습니다.
Tasks
Open-Domain Question AnsweringQuestion AnsweringRetrievalRetrieval-augmented GenerationSentenceSimilar Papers 제목 키워드 기반
SciDiagramEdit: Learning to Edit Scientific Diagrams from Paper Revisions
Editing the figures in a research paper is a routine and time-consuming part of everyday research practice: authors relabel components, rearrange panels, and restyle visuals as they revise their manuscripts. Automating t…
Multi-Granularity Guided Fusion-in-Decoder
In Open-domain Question Answering (ODQA), it is essential to discern relevant contexts as evidence and avoid spurious ones among retrieved results. The model architecture that uses concatenated multiple contexts in the d…
DecoderMulti-Task LearningNatural QuestionsOpen-Domain Question Answering+6DiagramEval: Evaluating LLM-Generated Diagrams via Graphs
Diagrams play a central role in research papers for conveying ideas, yet they are often notoriously complex and labor-intensive to create. Although diagrams are presented as images, standard image generative models strug…
Concept2Box: Joint Geometric Embeddings for Learning Two-View Knowledge Graphs
Knowledge graph embeddings (KGE) have been extensively studied to embed large-scale relational data for many real-world applications. Existing methods have long ignored the fact many KGs contain two fundamentally differe…
Knowledge Graph EmbeddingsKnowledge GraphsYNU-HPCC at SemEval-2020 Task 10: Using a Multi-granularity Ordinal Classification of the BiLSTM Model for Emphasis Selection
In this study, we propose a multi-granularity ordinal classification method to address the problem of emphasis selection. In detail, the word embedding is learned from Embeddings from Language Model (ELMO) to extract fea…
Language ModelingLanguage ModellingOrdinal Classification