Results 41 to 50 of about 1,322 (154)
Towards Robust Ranker for Text Retrieval
Findings of the Association for Computational Linguistics: ACL 2023, 2023 A ranker plays an indispensable role in the de facto 'retrieval & rerank' pipeline, but its training still lags behind -- learning from moderate negatives or/and serving as an auxiliary module for a retriever. In this work, we first identify two major barriers to a robust ranker, i.e., inherent label noises caused by a well-trained retriever and ...
Yucheng Zhou 0001 +7 more
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Adversarial Retriever-Ranker for dense text retrieval
CoRR, 2021 Current dense text retrieval models face two typical challenges. First, they adopt a siamese dual-encoder architecture to encode queries and documents independently for fast indexing and searching, while neglecting the finer-grained term-wise interactions. This results in a sub-optimal recall performance. Second, their model training highly relies on a
Hang Zhang 0029 +5 more
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Bioengineering, 2022 The eye is generally considered to be the most important sensory organ of humans. Diseases and other degenerative conditions of the eye are therefore of great concern as they affect the function of this vital organ. With proper early diagnosis by experts
Ahmed Al Marouf +4 more
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R1-Ranker: Teaching LLM Rankers to Reason
Large language models (LLMs) have recently shown strong reasoning abilities in domains like mathematics, coding, and scientific problem-solving, yet their potential for ranking tasks, where prime examples include retrieval, recommender systems, and LLM routing, remains underexplored.
Feng, Tao +6 more
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Vasily Rozanov’s philosophy of low ranker
RUDN Journal of Philosophy, 2013 The article based on Vasily Rosanov’s writings of various periods makes the conclusion that his philosophy must be understand not as «pansexualism», but as the continuation of Russian thought with it’s traditions of heart love, pity, nonviolence ...
E S Grevtsova
doaj
Re-Rankers as Relevance Judges
CoRRUsing large language models (LLMs) to predict relevance judgments has shown promising results. Most studies treat this task as a distinct research line, e.g., focusing on prompt design for predicting relevance labels given a query and passage. However, predicting relevance judgments is essentially a form of relevance prediction, a problem extensively ...
Chuan Meng +5 more
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Forestist, 1981 Im Nord - Thraklen sin d die Ortsueise verbreltende Granit-, Granodiorit - und Quarzdiorit als Granit bezeichnet. Grösste Granitgebiet 'liegt in der Umgebung von Demirköy.
M. Doğan KANTARCI
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A Simple Lexicographic Ranker and Probability Estimator [PDF]
, 2007 Given a binary classification task, a ranker sorts a set of instances from highest to lowest expectation that the instance is positive. We propose a lexicographic ranker, LexRank , whose rankings are derived not from scores, but from a simple ranking of attribute values obtained from the training data.
Peter A. Flach, Edson Takashi Matsubara
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FairBayRank: A Fair Personalized Bayesian Ranker
ESANN 2023 proceesdings, 2023 Recommender systems are data-driven models that successfully pro- vide users with personalized rankings of items (movies, books...). Meanwhile, for user minority groups, those systems can be unfair in predicting users’ expectations due to biased data. Consequently, fairness remains an open challenge in the rank- ing prediction task.
Noulapeu Ngaffo, Armielle +3 more
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