Papers with indexing
Spacerini: Plug-and-play Search Engines with Pyserini and Hugging Face (2023.emnlp-demo)
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Christopher Akiki, Odunayo Ogundepo, Aleksandra Piktus, Xinyu Zhang, Akintunde Oladipo, Jimmy Lin, Martin Potthast
| Challenge: | a toolkit for reproducible information retrieval research is available for free. |
| Approach: | They present a tool that integrates Pyserini and Hugging Face to enable the seamless construction and deployment of interactive search engines. |
| Outcome: | The proposed tool makes state-of-the-art retrieval models more accessible to non-IR practitioners while minimizing deployment effort. |
XTR meets ColBERTv2: Adding ColBERTv2 Optimizations to XTR (2025.coling-industry)
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| Challenge: | XTR eliminates the need for multi-stage retrieval, but doesn't incorporate efficiency optimizations from ColBERTv2 which improve indexing and retrieval speed. |
| Approach: | They propose a multi-vector retrieval method that simplifies retrieval into a single stage through a modified learning objective. |
| Outcome: | The proposed method eliminates the need for multistage retrieval but doesn't incorporate efficiency optimizations from ColBERTv2 which improve indexing and retrieval speed. |
Optimizing Retrieval-Augmented Generation for E-Commerce How-To Assistance (2026.acl-industry)
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| Challenge: | Recent advances in large language models (LLMs) have significantly improved Retrieval-Augmented Generation (RAG), enabling assistants that can reliably ground responses in external knowledge sources while maintaining high-quality natural language interaction. |
| Approach: | They propose a RAG-based How-To Assistant that groundes responses in a proprietary knowledge base to provide personalized customer support. |
| Outcome: | The proposed assistant can ground responses in a proprietary knowledge base while maintaining high-quality natural language interaction. |
DyVo: Dynamic Vocabularies for Learned Sparse Retrieval with Entities (2024.emnlp-main)
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| Challenge: | Learned Sparse Retrieval (LSR) models use vocabularies from pre-trained transformers, which often split entities into nonsensical fragments. |
| Approach: | They propose to enhance the LSR vocabulary with Wikipedia concepts and entities, enabling the model to resolve ambiguities more effectively and stay current with evolving knowledge. |
| Outcome: | The proposed model outperforms state-of-the-art models across three entity-rich document ranking datasets. |
How Does Generative Retrieval Scale to Millions of Passages? (2023.emnlp-main)
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Ronak Pradeep, Kai Hui, Jai Gupta, Adam Lelkes, Honglei Zhuang, Jimmy Lin, Donald Metzler, Vinh Tran
| Challenge: | generative retrieval is a new paradigm for information retrieval, enabling a sequence-to-sequence model with a single Transformer . generative encoders have been used on small corpora, but only on large ones . |
| Approach: | They propose to encode an entire document corpus within a single Transformer . they find generative retrieval is competitive with state-of-the-art dual encoders on small corpora . |
| Outcome: | The proposed approach is competitive with state-of-the-art dual encoders on small corpora, the study finds . the proposed approach only evaluates on document corporales on the order of 100K in size . |
HopRAG: Multi-Hop Reasoning for Logic-Aware Retrieval-Augmented Generation (2025.findings-acl)
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| Challenge: | Traditional retrieval systems focus on lexical or semantic similarity rather than logical relevance. |
| Approach: | They propose a new RAG framework that augments retrieval with logical reasoning . hopRAG uses a retrieve-reason-prune mechanism to explore multi-hop neighbors . |
| Outcome: | The proposed framework outperforms conventional retrieval systems and state-of-the-art benchmarks on multi-hop QA tasks. |
Index-Time Prefix Injection for Multi-Tenant Retrieval: Improving Search Relevance Without Model Fine-Tuning (2026.acl-industry)
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| Challenge: | a single multilingual biencoder handles all retrieval, but these are task-generic and domain-agnostic. |
| Approach: | They propose a training-free method that prepending domain-descriptive prefixes to documents during indexing. |
| Outcome: | The proposed method improves retrieval relevance by prepending natural-language prefixes to documents during indexing. |
Unifying Multimodal Retrieval via Document Screenshot Embedding (2024.emnlp-main)
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| Challenge: | Existing document retrieval pipelines require document parsing and content extraction to prepare input for indexing. |
| Approach: | They propose a retrieval paradigm that regards document screenshots as a unified input format . they leverage a large vision-language model to directly encode document screenshot into dense representations . |
| Outcome: | The proposed method outperforms existing retrieval pipelines in a text-intensive context. |
PDF-to-Tree: Parsing PDF Text Blocks into a Tree (2024.findings-emnlp)
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| Challenge: | Existing studies try to extract one universal reading order for PDF files, however, some applications, like Retrieval Augmented Generation, require breaking long articles into sections and subsections for better indexing. |
| Approach: | They propose a new task and dataset, PDF-to-Tree, which organizes the text blocks of a PDF into a tree structure. |
| Outcome: | The proposed parser achieves 93.93% accuracy, surpassing baseline methods by 6.72%. |
Multi-Task Knowledge Distillation with Embedding Constraints for Scholarly Keyphrase Boundary Classification (2023.emnlp-main)
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| Challenge: | Scholarly keyphrase boundary classification is the task of identifying highly summative phrases from scientific papers and classifying them into a set of predefined classes. |
| Approach: | They propose a constraint which enforces the teachers and student similarity in the embedding space. |
| Outcome: | The proposed constraint outperforms previous studies and strong baselines on three datasets of scientific documents. |
Infrastructure for Semantic Annotation in the Genomics Domain (2020.lrec-1)
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Mahmoud El-Haj, Nathan Rutherford, Matthew Coole, Ignatius Ezeani, Sheryl Prentice, Nancy Ide, Jo Knight, Scott Piao, John Mariani, Paul Rayson, Keith Suderman
| Challenge: | a novel infrastructure for biomedical text mining combines NLP and corpus linguistics methods to provide a comprehensive corpus for literature-based discovery. |
| Approach: | They propose a novel pipeline for the collection, annotation, storage, retrieval and analysis of biomedical and life sciences literature . it uses an updatable Gene Ontology Semantic Tagger and a NLP pipeline scheduler to collect and process the corpus. |
| Outcome: | The proposed infrastructure allows for extreme-scale research on the open access PubMed Central archive. |
Infini-gram mini: Exact n-gram Search at the Internet Scale with FM-Index (2025.emnlp-main)
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| Challenge: | Modern language models are trained on text data downsampled from massive text corpora like Common Crawl. |
| Approach: | They propose an efficient and scalable system that can make petabyte-level text corpora searchable by using the FM-index data structure. |
| Outcome: | The proposed system indexes 83TB of Internet text in 99 days with a single 128-core CPU node (or 19 hours if using 137 such nodes). |