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Found 6 papers, 2 papers with code
Refining an Almost Clean Translation Memory Helps Machine Translation
While recent studies have been dedicated to cleaning very noisy parallel corpora to improve Machine Translation training, we focus in this work on filtering a large and mostly clean Translation Memory.
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.
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.
NATURE: Natural Auxiliary Text Utterances for Realistic Spoken Language Evaluation
We apply NATURE to common slot-filling and intent detection benchmarks and demonstrate that simple perturbations from the standard evaluation set by NATURE can deteriorate model performance significantly.
