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Found 5 papers, 3 papers with code
ExaRanker-Open: Synthetic Explanation for IR using Open-Source LLMs
ExaRanker recently introduced an approach to training information retrieval (IR) models, incorporating natural language explanations as additional labels.
InRanker: Distilled Rankers for Zero-shot Information Retrieval
Despite multi-billion parameter neural rankers being common components of state-of-the-art information retrieval pipelines, they are rarely used in production due to the enormous amount of compute required for inference.
BLUEX: A benchmark based on Brazilian Leading Universities Entrance eXams
One common trend in recent studies of language models (LMs) is the use of standardized tests for evaluation.
ExaRanker: Explanation-Augmented Neural Ranker
Recent work has shown that inducing a large language model (LLM) to generate explanations prior to outputting an answer is an effective strategy to improve performance on a wide range of reasoning tasks.
NeuralSearchX: Serving a Multi-billion-parameter Reranker for Multilingual Metasearch at a Low Cost
We compare our system with Microsoft's Biomedical Search and show that our design choices led to a much cost-effective system with competitive QPS while having close to state-of-the-art results on a wide range of public benchmarks.
