Papers Zero-shot Text Search
“Zero-shot Text Search” 태그가 달린 논문 14편 · 필터 해제
BM25S: Orders of magnitude faster lexical search via eager sparse scoring
We introduce BM25S, an efficient Python-based implementation of BM25 that only depends on Numpy and Scipy. BM25S achieves up to a 500x speedup compared to the most popular Python-based framework by eagerly computing BM25…
Passage RetrievalRetrievalText RetrievalZero-shot Text SearchBlended RAG: Improving RAG (Retriever-Augmented Generation) Accuracy with Semantic Search and Hybrid Query-Based Retrievers
Retrieval-Augmented Generation (RAG) is a prevalent approach to infuse a private knowledge base of documents with Large Language Models (LLM) to build Generative Q\&A (Question-Answering) systems. However, RAG accuracy b…
Information RetrievalOpen-Domain Question AnsweringQuestion Answering+4RetroMAE v2: Duplex Masked Auto-Encoder For Pre-Training Retrieval-Oriented Language Models
To better support retrieval applications such as web search and question answering, growing effort is made to develop retrieval-oriented language models. Most of the existing works focus on improving the semantic represe…
Dimensionality ReductionInformation RetrievalQuestion AnsweringRetrieval+2COCO-DR: Combating Distribution Shifts in Zero-Shot Dense Retrieval with Contrastive and Distributionally Robust Learning
We present a new zero-shot dense retrieval (ZeroDR) method, COCO-DR, to improve the generalization ability of dense retrieval by combating the distribution shifts between source training tasks and target scenarios. To mi…
Language ModelingLanguage ModellingRetrievalText Retrieval+1SGPT: GPT Sentence Embeddings for Semantic Search
Decoder transformers have continued increasing in scale reaching hundreds of billions of parameters. Due to their scale the same decoder sets state-of-the-art results on various language tasks via prompting or fine-tunin…
Argument RetrievalBiomedical Information RetrievalCitation PredictionDecoder+11Text and Code Embeddings by Contrastive Pre-Training
Text embeddings are useful features in many applications such as semantic search and computing text similarity. Previous work typically trains models customized for different use cases, varying in dataset choice, trainin…
Code SearchLinear-Probe ClassificationNatural QuestionsPassage Ranking+3Large Dual Encoders Are Generalizable Retrievers
It has been shown that dual encoders trained on one domain often fail to generalize to other domains for retrieval tasks. One widespread belief is that the bottleneck layer of a dual encoder, where the final score is sim…
Domain GeneralizationPassage RetrievalRetrievalSentence Retrieval+1GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval
Dense retrieval approaches can overcome the lexical gap and lead to significantly improved search results. However, they require large amounts of training data which is not available for most domains. As shown in previou…
Domain AdaptationRetrievalUnsupervised Domain AdaptationZero-shot Text SearchColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction
Neural information retrieval (IR) has greatly advanced search and other knowledge-intensive language tasks. While many neural IR methods encode queries and documents into single-vector representations, late interaction m…
Information RetrievalOpen-Domain Question AnsweringPassage RetrievalRetrieval+2SPLADE v2: Sparse Lexical and Expansion Model for Information Retrieval
In neural Information Retrieval (IR), ongoing research is directed towards improving the first retriever in ranking pipelines. Learning dense embeddings to conduct retrieval using efficient approximate nearest neighbors …
Information RetrievalRetrievalZero Shot on BEIR (Inference Free Model)Zero-shot Text SearchEfficiently Teaching an Effective Dense Retriever with Balanced Topic Aware Sampling
A vital step towards the widespread adoption of neural retrieval models is their resource efficiency throughout the training, indexing and query workflows. The neural IR community made great advancements in training effe…
GPURe-RankingRetrievalText Retrieval+1Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval
Conducting text retrieval in a dense learned representation space has many intriguing advantages over sparse retrieval. Yet the effectiveness of dense retrieval (DR) often requires combination with sparse retrieval. In t…
Contrastive LearningPassage RetrievalRerankingRetrieval+2MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers
Pre-trained language models (e.g., BERT (Devlin et al., 2018) and its variants) have achieved remarkable success in varieties of NLP tasks. However, these models usually consist of hundreds of millions of parameters whic…
Sentence RetrievalZero-shot Text SearchZero-Shot Question Generation from Knowledge Graphs for Unseen Predicates and Entity Types
We present a neural model for question generation from knowledge base triples in a "Zero-Shot" setup, that is generating questions for triples containing predicates, subject types or object types that were not seen at tr…
DecoderKnowledge GraphsQuestion GenerationQuestion-Generation+1