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Found 12 papers, 10 papers with code
NoMIRACL: Knowing When You Don't Know for Robust Multilingual Retrieval-Augmented Generation
We measure LLM robustness using two metrics: (i) hallucination rate, measuring model tendency to hallucinate an answer, when the answer is not present in passages in the non-relevant subset, and (ii) error rate, measuring model inaccuracy to recognize relevant passages in the relevant subset.
Leveraging LLMs for Synthesizing Training Data Across Many Languages in Multilingual Dense Retrieval
Therefore, to study model capabilities across both cross-lingual and monolingual retrieval tasks, we develop SWIM-IR, a synthetic retrieval training dataset containing 33 (high to very-low resource) languages for training multilingual dense retrieval models without requiring any human supervision.
HAGRID: A Human-LLM Collaborative Dataset for Generative Information-Seeking with Attribution
In this paper, we introduce a new dataset, HAGRID (Human-in-the-loop Attributable Generative Retrieval for Information-seeking Dataset) for building end-to-end generative information-seeking models that are capable of retrieving candidate quotes and generating attributed explanations.
SPRINT: A Unified Toolkit for Evaluating and Demystifying Zero-shot Neural Sparse Retrieval
In this work, we provide SPRINT, a unified Python toolkit based on Pyserini and Lucene, supporting a common interface for evaluating neural sparse retrieval.
Resources for Brewing BEIR: Reproducible Reference Models and an Official Leaderboard
BEIR is a benchmark dataset for zero-shot evaluation of information retrieval models across 18 different domain/task combinations.
Evaluating Embedding APIs for Information Retrieval
The ever-increasing size of language models curtails their widespread availability to the community, thereby galvanizing many companies into offering access to large language models through APIs.
Simple Yet Effective Neural Ranking and Reranking Baselines for Cross-Lingual Information Retrieval
The advent of multilingual language models has generated a resurgence of interest in cross-lingual information retrieval (CLIR), which is the task of searching documents in one language with queries from another.
Making a MIRACL: Multilingual Information Retrieval Across a Continuum of Languages
MIRACL (Multilingual Information Retrieval Across a Continuum of Languages) is a multilingual dataset we have built for the WSDM 2023 Cup challenge that focuses on ad hoc retrieval across 18 different languages, which collectively encompass over three billion native speakers around the world.
Injecting Domain Adaptation with Learning-to-hash for Effective and Efficient Zero-shot Dense Retrieval
In our work, we evaluate LTH and vector compression techniques for improving the downstream zero-shot retrieval accuracy of the TAS-B dense retriever while maintaining efficiency at inference.
GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval
This limits the usage of dense retrieval approaches to only a few domains with large training datasets.
Ranked #9 on
Zero-shot Text Search
on BEIR
BEIR: A Heterogenous Benchmark for Zero-shot Evaluation of Information Retrieval Models
To address this, and to facilitate researchers to broadly evaluate the effectiveness of their models, we introduce Benchmarking-IR (BEIR), a robust and heterogeneous evaluation benchmark for information retrieval.
Ranked #1 on
Argument Retrieval
on ArguAna (BEIR)
Augmented SBERT: Data Augmentation Method for Improving Bi-Encoders for Pairwise Sentence Scoring Tasks
Bi-encoders, on the other hand, require substantial training data and fine-tuning over the target task to achieve competitive performance.
